Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.

The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks

HARIRAYA: a novel breast cancer pseudo-color feature for multimodal mammogram using deep learning

Breast cancer is the leading cancer in the world. Mammogram is a gold standard for detecting breast cancer at earlier screening because of its sensitivity. Standard grayscale mammogram images are used by expert radiologists and Computer Aided-Diagnosis (CAD) systems. Yet, this original x-ray color provides little information to human radiologists and CAD systems to make decision. This binary color code thus affects sensitivity and specificity of prediction and subsequently affects accuracy. In order to enhance classifier models’ perfor-mance, this paper proposes a novel feature-level data integration method that combines features from grayscale mammogram and spec- trum mammogram based on a deep neural network (DNN), called HARIRAYA. Pseudo-color is generated using spectrum color code to produce Spectrum mammogram from grayscale mammogram. The DNN is trained with three layers: grayscale, false-color and joint fea-ture representation layers. Empirical results show that the multi-modal DNN model has a better performance in the prediction of malig- nant breast tissue than single-modal DNN using HARIRAYA features

3D homologous multi-points warping application to sexual dimorphism in human face

Sexual dimorphism in Homo-sapiens is a phenomenon of a direct product of evolution by natural selection where evolutionary forces acted separately on the sexes which brought about the differences in appearance between male and female such as in shape and size. This study investigates sexual dimorphism in human face with the application of Automatic Homologous Multi-points Warping (AHMW) for 3D facial landmark by building a template mesh as a reference object which is thereby applied to each of the target mesh on Stirling/ESRC dataset containing 101 subjects (male = 47, female = 54). The semi-landmarks are subjected to sliding along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal. Principal Component Analysis (PCA) is used for feature selection and the features are classified using Linear Discriminant Analysis (LDA) with an accuracy of 99.01%

Landmark-based multi-points warping approach to 3D facial expression recognition in human

Expression in H-sapiens plays a remarkable role when it comes to social communication. The identification of this expression by human beings is relatively easy and accurate. However, achieving the same result in 3D by machine remains a challenge in computer vision. This is due to the current challenges facing facial data acquisition in 3D: such as lack of homology and complex mathematical analysis for facial point digitization. This study proposes facial expression recognition in human with the application of Multi-points Warping for 3D facial landmark. The results indicate that Fear expression has the lowest recognition accuracy while Surprise expression has the highest recognition accuracy. The classifier achieved a recognition accuracy of 99.58%

Homologous multi-points warping: an algorithm for automatic 3D facial landmark

Over the decade scientists have been researching to know whether face recognition is performed holistically or with local feature analysis which has led to the proposition of various advanced methods in face recognition, especially using facial landmark. The current facial landmark methods in 3D are mathematically complex, contain insufficient landmarks, lack homology and full of localization error due to manual annotation. This paper proposes an Automatic Homologous Multi-Points Warping (AHMW) for 3D facial landmarking, experimented on three datasets using 500 landmarks (16 anatomical fixed points and 484 sliding semi-landmarks) by building a template mesh as a reference object and thereby applies the template to each of the targets on three datasets. The results show that the method is robust with minimum localization error (Stirling/ESRC:0.077; Bosphorus:0.088; and FRGC v2: 0.083)

A requirement engineering model for big data software

Most prevailing software engineering methodologies assume that software systems are developed from scratch to capture business data and subsequently generate reports. Nowadays, massive data may exist even before software systems are developed. These data may also be freely available on Internet or may present in silos in organizations. The advancement in artificial intelligence and computing power has also prompted the need for big data analytics to unleash more business values to support evidence-based decisions. Some business values are less evident than others, especially when data are analyzed in silos. These values could be potentially unleashed and augmented from the insights discovered by data scientists through data mining process. Data mining may involve overlaying and merging data from different sources to extract data patterns. Ideally, these values should be eventually incorporated into the information systems to be. To realize this, we propose that software engineers ought to elicit software requirements together with data scientists. However, in the traditional software engineering process, such collaboration and business values are usually neglected. In this paper, we present a new requirement engineering model that allows software engineers and data scientists to discover these values hand in hand as part of software requirement process. We also demonstrate how the proposed requirement model captures and expresses business values that unleashed through big data analytics using an adapted use case diagram

Detection of pulmonary tuberculosis manifestation in chest x-rays using different Convolutional Neural Network (CNN) models

Tuberculosis (TB) is airborne infectious disease which has claimed many lives than any other infectious disease. Chest X-rays (CXRs) are often used in recognizing TB manifestation site in chest. Lately, CXRs are taken in digital formats, which has made a huge impact in rapid diagnosis using automated systems in medical field. In our current work, four simple Convolutional Neural Networks (CNN) models such as VGG-16, VGG-19, RestNet50, and GoogLenet are implemented in identification of TB manifested CXRs. Two public TB image datasets were utilized to conduct this research. This study was carried out to explore the limit of accuracies and AUCs acquired by simple and small-scale CNN with complex and large-scale CNN models. The results achieved from this work are compared with results of two previous studies. The results indicate that our proposed VGG-16 model has gained highest score overall compared to the models from other two previous studies

Feature selection optimization using hybrid relief-f with self-adaptive differential evolution

In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. Thus, feature selection plays an important role in overcoming dimensionality problem. Relief-f is one of the filter methods to rank the most significant features based on their relevance. Although relief-f proved to be a powerful technique in filter strategy, but this method only rank the features based on their significant level. Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. Simple and powerful in implementation, we combined relief-f with DE in our proposed feature selection method to solving the optimization problem. In this work, population size and generation size were adaptively determined from the number of features from relief-f. The performance of proposed method is compared with several feature selection techniques in order to prove their superiority using ten datasets obtained from UCI machine learning repository

Akhlak-Based Intelligent Multi-Agent Architecture

Coordination in socially intelligent agents is imperative because actions produced by the agents, without any deliberation on its consequences, may have non-local effects. These non-local effects will lead to a possibility of inefficient performance of the individual and the system. Socially intelligent agents can be best described as autonomous problem solvers that have to achieve their objectives by interacting with other similarly autonomous entities. The reason that such agents are inherently social makes the agents must produce decisions that are not only rational from the perspective of the individual agent but also rational from the perspective of the society. To solve the above problems we propose Akhlak coordination model stems from Akhlak concept to help and guide the agents to coordinate their tasks with other members. The model will be design as a social component of an agent-based architecture called Multi-agent Linkage Social Intelligent (MALSI) architecture. MALSI architecture that associated with Akhlak model is realized in an exemplar computational setting: a case study and a series of experiments are made of the relative performance of the model functions in a two different environment: homogeneous and heterogeneous environments. The case study measures performance of the system aided MALSI agent (embedded with Akhlak model) in perspective of users and designers of the system. The experiments measure coherency of multi-agent, which mean, how well the agents behaves as a unit, along some dimension of evaluation. The case study and the experiments are conducted in real-life industry in government agencies: Malaysian Administrative Modernization and Management Planning Unit (MAMPU) and Ministry of Education (MOE)