Oludayo O. Olugbara

EXPLOITING IMAGE CONTENT IN LOCATION-BASED SHOPPING RECOMMENDER SYSTEMS FOR MOBILE USERS

This paper demonstrates how image content can be used to realize a location-based shopping recommender system for intuitively supporting mobile users in decision making. Generic Fourier Descriptors (GFD) image content of an item was extracted to exploit knowledge contained in item and user profile databases for learning to rank recommendations. Analytic Hierarchy Process (AHP) was used to automatically select a query item from a user profile. Single Criterion Decision Ranking (SCDR) and Multiple-Criteria Decision-Ranking (MCDR) techniques were compared to study the effect of multidimensional ratings of items on recommendations effectiveness. The SCDR and MCDR techniques are, respectively, based on Image Content Similarity Score (ICSS) and Relative Ratio (RR) aggregating function. Experimental results of a real user study showed that an MCDR system increases user satisfaction and improves recommendations effectiveness better than an SCDR system.

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error.

Pixel intensity clustering algorithm for multilevel image segmentation

Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification

The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.

Minimizing Energy Consumption in Wireless Ad hoc Networks with Meta heuristics

Abstract The objective of this study is to describe an energy function model base on Geographic Adaptive Fidelity (GAF), which is one of the best known topology management schemes used in saving energy consumption in ad-hoc wireless networks. In wireless ad-hoc network, the nodes responsible for the transmission of data are battery-operated and as a result, there is a need for energy to be conserved in order to prolong the battery lifespan. Genetic Algorithm (GA) and Simulated Annealing (SA) metaheuristics are compared to minimize the energy consumption in ad-hoc wireless networks modelled by rectangular GAF. Results show that GA and SA meta-heuristics are useful optimization techniques for minimizing the energy consumption in ad-hoc wireless networks.

Transforming voting paradigm — The shift from inline through online to mobile voting

Traditional poll-site voting methods poise multiple administrative and logistical challenges inter alia scalability, cost and miscount. Moreover, there is a noticeable decline in the turnout rate of eligible voters, particularly the youth. This work proposes a novel mobile voting model that uses common-off-the-shelf (COTS) mobile phones, in conjunction with a Near Field Communication (NFC) tag technology and a pragmatic biometric verification scheme. The mobile voting application being proposed in this work is launched by leveraging the auto-coupling capability of NFC, which also serves for storing baseline information about voters. The auto-coupling feature mediates device familiarity requirement, which is a limiting factor for using mobile phones to administer elections satisfying transparency and ease of use. The baseline information stored in the NFC tag provides local biometric reference data that mediate intensive bandwidth consumption, computational requirement, provide for match-on-a-card features and satisfy the constraint that only the eligible voter may vote. This work notes all security requirements for this model and addresses some architecture, design and security issues that will arise if such a choice is made.

Experimentation using short-term spectral features for secure mobile internet voting authentication

We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker recognition systems. In this study, higher dimensions of each of the short-term features were reduced to an 81-element feature vector per Speaker using Histogram of Oriented Gradients (HOG) algorithm while neural network ensemble was utilized as the pattern matching algorithm. Out of the four short-term spectral features investigated, the LPCC-HOG gave the best statistical results with statistic of 0.9127 and mean square error of 0.0407. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting architecture we are proposing in this paper.

CLAHE Inspired Segmentation of Dermoscopic Images Using Mixture of Methods

The overarching objective of this study is to segment lesion areas of the surrounding healthy skin. The localization of the actual lesion area is an important step towards the automation of a diagnostic system for discriminating between malignant and benign lesions. We have applied a combination of methods, including intensity equalization, thresholding, morphological operation and GrabCut algorithm to segment the lesion area in a dermoscopic image. The result shows that the approach used in the study is effective in localizing lesion pixels in a dermoscopic image. This would aid the selection of discriminating features for the classification of malignancy of a given dermoscopic image.

An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem.