Detector Design Considerations in High-Dimensional Artificial Immune Systems

This research lays the groundwork for a network intrusion detection system that can operate with only knowledge of normal network traffic, using a process known as anomaly detection. Real-valued negative selection (RNS) is a specific anomaly detection algorithm that can be used to perform two-class classification when only one class is available for training. Researchers have shown fundamental problems with the most common detector shape, hyperspheres, in high-dimensional space. The research contained herein shows that the second most common detector type, hypercubes, can also cause problems due to biasing certain features in high dimensions. To address these problems, a new detector shape, the hypersteinmetz solid, is proposed, the goal of which is to provide a tradeoff between the problems plaguing hyperspheres and hypercubes. In order to investigate the potential benefits of the hypersteinmetz solid, an effective RNS detector size range is determined. Then, the relationship between content coverage of a dataset and classification accuracy is investigated. Subsequently, this research shows the tradeoffs that take place in high-dimensional data when hypersteinmetzes are chosen over hyperspheres or hypercubes. The experimental results show that detector shape is the dominant factor toward classification accuracy in high-dimensional RNS

Adapting Artificial Immune Algorithms For University Timetabling

Penjadualan kelas dan peperiksaan di universiti adalah masalah pengoptimuman berkekangan tinggi. University class and examination timetabling are highly constrained optimization problems

Wisdom at Work: The Importance of the Older and Experienced Nurse in the Workplace

Focuses on promising strategies and opportunities for retaining experienced nurses, one of many approaches the authors recommend to alleviate the current nurse shortage crisis

Interval-valued upside potential and downside risk portfolio optimisation

A novel interval optimisation approach is developed to include imprecise forecasts into the portfolio selection process for investors measuring upside potential and downside risk as deviations from a target return. Crisp scenarios are substituted by interval scenarios and the resulting interval optimisation problem is solved in a tractable manner by means of a bi-objective formulation exploiting a partial order relation between intervals. Four utility case studies involving assets from the F.T.S.E. M.I.B. Index are considered to illustrate how impreciseness can be efficiently handled in portfolio management

Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs