Noura Al Moubayed

A novel smart multi-objective particle swarm optimisation using decomposition

A novel Smart Multi-Objective Particle Swarm Optimisation method - SDMOPSO - is presented in the paper. The method uses the decomposition approach proposed in MOEA/D, whereby a multiobjective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. The paper also introduces a novel smart approach for sharing information between particles, whereby each particle calculates a new position in advance using its neighbourhood information and shares this new information with the swarm. The results of applying SDMOPSO on five standard MOPs show that SDMOPSO is highly competitive comparing with two state-of-the-art algorithms.

D 2 MOPSO: multi-objective particle swarm optimizer based on decomposition and dominance

D2MOPSO is a multi-objective particle swarm optimizer that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of aggregation problems, solving these problems simultaneously, within the PSO framework, might lead to premature convergence because of the leader selection process which uses the aggregation value as a criterion. Dominance plays a major role in building the leaders archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 10 standard MOPs show D2MOPSO outperforms two state-of-the-art decomposition based evolutionary methods.

Signal Generation for Search-Based Testing of Continuous Systems

Test case generation constitutes a critical activity in software testing that is cost-intensive, time-consuming and error-prone when done manually. Hence, an automation of this process is required. One automation approach is search-based testing for which the task of generating test data is transformed into an optimization problem which is solved using metaheuristic search techniques. However, only little work has been done so far applying search-based testing techniques to systems that depend on continuous input signals.This paper proposes two novel approaches to generating input signals from within search-based testing techniques for continuous systems. These approaches are then shown to be very effective when experimentally applied to the problem of approximating a set of realistic signals.

Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces

In [1], we introduced Smart Multi-Objective Particle Swarm Optimisation using Decomposition (SDMOPSO). The method uses the decomposition approach proposed in Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D), whereby a multi-objective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. This work customize SDMOSPO to cover binary problems and applies the proposed binary method on the channel selection problem for Brain-Computer Interfaces(BCI).

Face-Based Automatic Personality Perception

Automatic Personality Perception is the task of automatically predicting the personality traits people attribute to others. This work presents experiments where such a task is performed by mapping facial appearance into the Big-Five personality traits, namely Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism. The experiments are performed over the pictures of the FERET corpus, originally collected for biometrics purposes, for a total of 829 individuals. The results show that it is possible to automatically predict whether a person is perceived to be above or below median with an accuracy close to 70 percent (depending on the trait).

Temporal White-Box Testing Using Evolutionary Algorithms

Embedded computer systems should fulfill real-time requirements which need to be checked in order to assure system quality. This paper stands to propose some ideas for testing the temporal behavior of real-time systems. The goal is to achieve white-box temporal testing using evolutionary techniques to detect system failures in reasonable time and little effort.

Multi-objective optimisation of cancer chemotherapy using smart PSO with decomposition

The paper presents a novel approach to optimising cancer chemotherapy with respect to conflicting treatment objectives aimed at reducing the number of cancerous cells and at limiting the amounts of anti-cancer drugs used. The approach is based on the Particle Swarm Optimisaion (PSO) algorithm that decomposes a multi-objective optimisation problem into several scalar aggregation problems, thereby reducing its complexity and enabling an effective application of Computational Intelligence techniques. The novelty of the algorithm is in providing particles in the swarm with information from a set of defined neighbours and leaders that assists in finding versatile chemotherapeutic treatments.

SMS Spam Filtering Using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of la- belled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97% accuracy which compares favourably to the best reported algorithms presented in the literature.

Continuous presentation for multi-objective channel selection in Brain-Computer Interfaces

A novel presentation for channel selection problem in Brain-Computer Interfaces (BCI) is introduced here. Continuous presentation in a projected two-dimensional space of the Electroencephalograph (EEG) cap is proposed. A multi-objective particle swarm optimization method (D2MOPSO) is employed where particles move in the EEG cap space to locate the optimum set of solutions that minimize the number of selected channels and the classification error rate. This representation focuses on the local relationships among EEG channels as the physical location of the channels is explicitly represented in the search space avoiding picking up channels that are known to be uncorrelated with the mental task. In addition continuous presentation is a more natural way for problem solving in PSO framework. The method is validated on 10 subjects performing right-vs-left motor imagery BCI. The results are compared to these obtained using Sequential Floating Forward Search (SFFS) and shows significant enhancement in classification accuracy but most importantly in the distribution of the selected channels.

Enhanced detection of movement onset in EEG through deep oversampling

A deep learning approach for oversampling of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and the detection of movement onset during online Brain-Computer Interfaces in particular. Learning from self-paced EEG data is challenging mainly due to the highly imbalance nature of the data reducing the generalisation power of the classification model. Oversampling of the movement class enhances the overall accuracy of an onset detection system by over 17%, p < 0.05, when tested on 12 subjects. Modelling the data using a deep neural network not only helps oversampling the movement class but also can help build a subject independent model of movement. In this work we present initial results on the applicability of this model.