Doctoral Degrees (Computer Science)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7113

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The enhanced best performance algorithm for global optimization with applications.
(2016) Chetty, Mervin.; Adewumi, Aderemi Oluyinka.
Abstract available in PDF file.
Hierarchical age estimation using enhanced facial features.
(2018) Angulu, Raphael.; Tapamo, Jules-Raymond.; Adewumi, Aderemi Oluyinka.
Ageing is a stochastic, inevitable and uncontrollable process that constantly affect shape, texture and general appearance of the human face. Humans can easily determine ones’ gender, identity and ethnicity with highest accuracy as compared to age. This makes development of automatic age estimation techniques that surpass human performance an attractive yet challenging task. Automatic age estimation requires extraction of robust and reliable age discriminative features. Local binary patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age discriminative features. Although local ternary patterns (LTP) is insensitive to noise, it uses a single static threshold for all images regardless of varied image conditions. Local directional patterns (LDP) uses k directional responses to encode image gradient and disregards not only central pixel in the local neighborhood but also 8 􀀀 k directional responses. Every pixel in an image carry subtle information. Discarding 8 􀀀 k directional responses lead to lose of discriminative texture features. This study proposes two variations of LDP operator for texture extraction. Significantorientation response LDP (SOR-LDP) encodes image gradient by grouping eight directional responses into four pairs. Each pair represents orientation of an edge with respect to central reference pixel. Values in each pair are compared and the bit corresponding to the maximum value in the pair is set to 1 while the other is set to 0. The resultant binary code is converted to decimal and assigned to the central pixel as its’ SOR-LDP code. Texture features are contained in the histogram of SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the difference between neighboring pixels and central pixel in 3 3 image region. These differential values are convolved with Kirsch edge detectors to obtain directional responses. These responses are normalized and used as probability of an edge occurring towards a respective direction. An adaptive threshold is applied to derive LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms of negative and positive LTDP encoded images are concatenated to obtain texture feature. Regardless of there being evidence of spatial frequency processing in primary visual cortex, biologically inspired features (BIF) that model visual cortex uses only scale and orientation selectivity in feature extraction. Furthermore, these BIF are extracted using holistic (global) pooling across scale and orientations leading to lose of substantive information. This study proposes multi-frequency BIF (MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol, this study investigated performance of proposed feature extractors in age estimation in a hierarchical way by performing age-group classification using Multi-layer Perceptron (MLP) followed by within age-group exact age regression using support vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were used to evaluate performance of proposed face descriptors. Experimental results on FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform state-of-the-art feature descriptors in age estimation. Experimental results show that performing gender discrimination before age-group and age estimation further improves age estimation accuracies. Shape, appearance, wrinkle and texture features are simultaneously extracted by visual system in primates for the brain to process and understand an image or a scene. However, age estimation systems in the literature use a single feature for age estimation. A single feature is not sufficient enough to capture subtle age discriminative traits due to stochastic and personalized nature of ageing. This study propose fusion of different facial features to enhance their discriminative power. Experimental results show that fusing shape, texture, wrinkle and appearance result into robust age discriminative features that achieve lower MAE compared to single feature performance.
Improved roach-based algorithms for global optimization problems.
(2014) Obagbuwa, Ibidun Christiana.; Adewumi, Aderemi Oluyinka.
Optimization of systems plays an important role in various fields including mathematics, economics, engineering and life sciences. A lot of real world optimization problems exist across field of endeavours such as engineering design, space planning, networking, data analysis, logistic management, financial planning, risk management, and a host of others. These problems are constantly increasing in size and complexity, necessitating the need for improved techniques. Many conventional approaches have failed to solve complex problems effectively due to increasingly large solution space. This has led to the development of evolutionary algorithms that draw inspiration from the process of natural evolution. It is believed that nature provides inspirations that can lead to innovative models or techniques for solving complex optimization problems. Among the class of paradigm based on this inspiration is Swarm Intelligence (SI). SI is one of the recent developments of evolutionary computation. A SI paradigm is comprised of algorithms inspired by the social behaviour of animals and insects. SI-based algorithms have attracted interest, gained popularity and attention because of their flexibility and versatility. SIbased algorithms have been found to be efficient in solving real world optimization problems. Examples of SI algorithms include Ant Colony Optimization (ACO) inspired by the pheromone trail-following behaviour of ant species; Particle Swarm Optimization (PSO) inspired by flocking and swarming behaviour of insects and animals; and Bee Colony Optimization (BCO) inspired by bees’ food foraging. Recent emerging techniques in SI includes Roach-based Algorithms (RBA) motivated by cockroaches social behaviour. Two recently introduced RBA algorithms are Roach Infestation Optimization (RIO) and Cockroach Swarm Optimization (CSO) which have been applied to some optimization problems to achieve competitive results when compared to PSO. This study is motivated by the promising results of RBA, which have shown that the algorithms have potentials to be efficient tools for solving optimization problems. Extensive studies of existing RBA were carried out in this work revealing the shortcomings such as slow convergence and entrapment in local minima. The aim of this study is to overcome the identified drawbacks. We investigate RBA variants that are introduced in this work by introducing parameters such as constriction factor and sigmoid function that have proved effective for similar evolutionary algorithms in the literature. In addition components such as vigilance, cannibalism and hunger are incorporated into existing RBAs. These components are constructed by the use of some known techniques such as simple Euler, partial differential equation, crossover and mutation methods to speed up convergence and enhance the stability, exploitation and exploration of RBA. Specifically, a stochastic constriction factor was introduced to the existing CSO algorithm to improve its performance and enhance its ability to solve optimization problems involving thousands of variables. A CSO algorithm that was originally designed with three components namely chase-swarming, dispersion and ruthlessness is extended in this work with hunger component to improve its searching ability and diversity. Also, predator-prey evolution using crossover and mutation techniques were introduced into the CSO algorithm to create an adaptive search in each iteration thereby making the algorithm more efficient. In creating a discrete version of a CSO algorithm that can be used to evaluate optimization problems with any discrete range value, we introduced the sigmoid function. Furthermore, a dynamic step-size adaptation with simple Euler method was introduced to the existing RIO algorithm enhancing swarm stability and improving local and global searching abilities. The existing RIO model was also re-designed with the inclusion of vigilance and cannibalism components. The improved RBA were tested on established global optimization benchmark problems and results obtained compared with those from the literature. The improved RBA introduced in this work show better improvements over existing ones.
Intelligent instance selection techniques for support vector machine speed optimization with application to e-fraud detection.
(2017) Akinyelu, Ayobami Andronicus.; Adewumi, Aderemi Oluyinka.
Decision-making is a very important aspect of many businesses. There are grievous penalties involved in wrong decisions, including financial loss, damage of company reputation and reduction in company productivity. Hence, it is of dire importance that managers make the right decisions. Machine Learning (ML) simplifies the process of decision making: it helps to discover useful patterns from historical data, which can be used for meaningful decision-making. The ability to make strategic and meaningful decisions is dependent on the reliability of data. Currently, many organizations are overwhelmed with vast amounts of data, and unfortunately, ML algorithms cannot effectively handle large datasets. This thesis therefore proposes seven filter-based and five wrapper-based intelligent instance selection techniques for optimizing the speed and predictive accuracy of ML algorithms, with a particular focus on Support Vector Machine (SVM). Also, this thesis proposes a novel fitness function for instance selection. The primary difference between the filter-based and wrapper-based technique is in their method of selection. The filter-based techniques utilizes the proposed fitness function for selection, while the wrapper-based technique utilizes SVM algorithm for selection. The proposed techniques are obtained by fusing SVM algorithm with the following Nature Inspired algorithms: flower pollination algorithm, social spider algorithm, firefly algorithm, cuckoo search algorithm and bat algorithm. Also, two of the filter-based techniques are boundary detection algorithms, inspired by edge detection in image processing and edge selection in ant colony optimization. Two different sets of experiments were performed in order to evaluate the performance of the proposed techniques (wrapper-based and filter-based). All experiments were performed on four datasets containing three popular e-fraud types: credit card fraud, email spam and phishing email. In addition, experiments were performed on 20 datasets provided by the well-known UCI data repository. The results show that the proposed filter-based techniques excellently improved SVM training speed in 100% (24 out of 24) of the datasets used for evaluation, without significantly affecting SVM classification quality. Moreover, experimental results also show that the wrapper-based techniques consistently improved SVM predictive accuracy in 78% (18 out of 23) of the datasets used for evaluation and simultaneously improved SVM training speed in all cases. Furthermore, two different statistical tests were conducted to further validate the credibility of the results: Freidman’s test and Holm’s post-hoc test. The statistical test results reveal that the proposed filter-based and wrapper-based techniques are significantly faster, compared to standard SVM and some existing instance selection techniques, in all cases. Moreover, statistical test results also reveal that Cuckoo Search Instance Selection Algorithm outperform all the proposed techniques, in terms of speed. Overall, the proposed techniques have proven to be fast and accurate ML-based e-fraud detection techniques, with improved training speed, predictive accuracy and storage reduction. In real life application, such as video surveillance and intrusion detection systems, that require a classifier to be trained very quickly for speedy classification of new target concepts, the filter-based techniques provide the best solutions; while the wrapper-based techniques are better suited for applications, such as email filters, that are very sensitive to slight changes in predictive accuracy.
A semantic sensor web framework for proactive environmental monitoring and control.
(2017) Adeleke, Jude Adekunle.; Moodley, Deshendran.; Rens, Gavin Brian.; Adewumi, Aderemi Oluyinka.
Observing and monitoring of the natural and built environments is crucial for main- taining and preserving human life. Environmental monitoring applications typically incorporate some sensor technology to continually observe specific features of inter- est in the physical environment and transmitting data emanating from these sensors to a computing system for analysis. Semantic Sensor Web technology supports se- mantic enrichment of sensor data and provides expressive analytic techniques for data fusion, situation detection and situation analysis. Despite the promising successes of the Semantic Sensor Web technology, current Semantic Sensor Web frameworks are typically focused at developing applications for detecting and reacting to situations detected from current or past observations. While these reactive applications provide a quick response to detected situations to minimize adverse effects, they are limited when it comes to anticipating future adverse situations and determining proactive control actions to prevent or mitigate these situations. Most current Semantic Sensor Web frameworks lack two essential mechanisms required to achieve proactive control, namely, mechanisms for antici- pating the future and coherent mechanisms for consistent decision processing and planning. Designing and developing proactive monitoring and control Semantic Sensor Web applications is challenging. It requires incorporating and integrating different tech- niques for supporting situation detection, situation prediction, decision making and planning in a coherent framework. This research proposes a coherent Semantic Sen- sor Web framework for proactive monitoring and control. It incorporates ontology to facilitate situation detection from streaming sensor observations, statistical ma- chine learning for situation prediction and Markov Decision Processes for decision making and planning. The efficacy and use of the framework is evaluated through the development of two different prototype applications. The first application is for proactive monitoring and control of indoor air quality to avoid poor air quality situations. The second is for proactive monitoring and control of electricity usage in blocks of residential houses to prevent strain on the national grid. These appli- cations show the effectiveness of the proposed framework for developing Semantic Sensor Web applications that proactively avert unwanted environmental situations before they occur.
Studies in particle swarm optimization technique for global optimization.
(2013) Martins, Arasomwan Akugbe.; Adewumi, Aderemi Oluyinka.
Abstract available in the digital copy.

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Browsing Doctoral Degrees (Computer Science) by Author "Adewumi, Aderemi Oluyinka."