Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

Hunting For Metamorphic JavaScript Malware

Internet plays a major role in the propagation of malware. A recent trend is the infection of machines through web pages, often due to malicious code inserted in JavaScript. From the malware writer’s perspective, one potential advantage of JavaScript is that powerful code obfuscation techniques can be applied to evade de- tection. In this research, we analyze metamorphic JavaScript malware. We compare the effectiveness of several static detection strategies and we quantify the degree of morphing required to defeat each of these techniques

Cohort study ON Neuroimaging, Etiology and Cognitive consequences of Transient neurological attacks (CONNECT): Study rationale and protocol

Background: Transient ischemic attacks (TIA) are characterized by acute onset focal neurological symptoms and complete recovery within 24hours. Attacks of nonfocal symptoms not fulfilling the criteria for TIA but lacking a clear alternative diagnosis are called transient neurological attacks (TNA). Although TIA symptoms are transient in nature, cognitive complaints may persist. In particular, attacks consisting of both focal and nonfocal symptoms (mixed TNA) have been found to be associated with an increased risk of dementia. We aim to study the prevalence, etiology and risk factors of cognitive impairment after TIA or TNA. Methods/Design: CONNECT is a prospective cohort study on cognitive function after TIA and TNA. In total, 150 patients aged ≤45years with a recent (<7days after onset) TIA or TNA and no history of stroke or dementia will be included. We will classify events as: TIA, nonfocal TNA, or mixed TNA. Known short lasting paroxysmal neurological disorders like migraine aura, seizures and Ménière disease are excluded from the diagnosis of TNA. Patients will complete a comprehensive neuropsychological assessment and undergo MRI <7days after the qualifying event and again after six months. The primary clinical outcomes will be cognitive function at baseline and six months after the primary event. Imaging outcomes include the prevalence and evolution of DWI lesions, white matter hyperintensities and lacunes, as well as resting state networks functionality and white matter microstructural integrity. Differences between types of event and DWI, as well as determinants of both clinical and imaging outcomes, will be assessed. Discussion: CONNECT can provide insight in the prevalence, etiology and risk factors of cognitive impairment after TIA and TNA and thereby potentially identify a new group of patients at increased risk of cognitive impairment

A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms. To this end, we developed "ForneyLab" (https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools "Edward" and "Stan". In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate Reasonin

The fusion and integration of virtual sensors

There are numerous sensors from which to choose when designing a mobile robot: ultrasonic, infrared, radar, or laser range finders, video, collision detectors, or beacon based systems such as the Global Positioning System. In order to meet the need for reliability, accuracy, and fault tolerance, mobile robot designers often place multiple sensors on the same platform, or combine sensor data from multiple platforms. The combination of the data from multiple sensors to improve reliability, accuracy, and fault tolerance is termed Sensor Fusion.;The types of robotic sensors are as varied as the properties of the environment that need to be sensed. to reduce the complexity of system software, Roboticists have found it highly desirable to adopt a common interface between each type of sensor and the system responsible for fusing the information. The process of abstracting the essential properties of a sensor is called Sensor Virtualization.;Sensor virtualization to date has focused on abstracting the properties shared by sensors of the same type. The approach taken by T. Henderson is simply to expose to the fusion system only the data from the sensor, along with a textual label describing the sensor. We extend Henderson\u27s work in the following manner. First, we encapsulate both the fusion algorithm and the interface layer in the virtual sensor. This allows us to build multi-tiered virtual sensor hierarchies. Secondly, we show how common fusion algorithms can be encapsulated in the virtual sensor, facilitating the integration and replacement of both physical and virtual sensors. Finally, we provide a physical proof of concept using monostatic sonars, vector sonars, and a laser range-finder

Deep Convolutional Attention based Bidirectional Recurrent Neural Network for Measuring Correlated Colour Temperature from RGB images

Information on the connected colour temperature, which affects the image due to the surrounding illumination, is critical, particularly for natural lighting and capturing images. Several methods are introduced to detect colour temperature precisely; however, the majority of them are difficult to use or may generate internal noise. To address these issues, this research developed a hybrid deep model that properly measures temperature from RGB images while reducing noise. The proposed study includes image collection, pre-processing, feature extraction and CCT evaluation. The input RGB pictures are initially generated in the CIE 1931 colour space. After that, the raw input samples are pre-processed to improve picture quality by performing image cropping and scaling, denoising by hybrid median-wiener filtering and contrast enhancement via Rectified Gamma-based Quadrant Dynamic Clipped Histogram Equalisation (RG_QuaDy_CHE). The colour and texture features are eliminated during pre-processing to obtain the relevant CCT-based information. The Local Intensity Grouping Order Pattern (LIGOP) operator extracts the texture properties. In contrast, the colour properties are extracted using the RGB colour space’s mean, standard deviation, skewness, energy, smoothness and variance. Finally, using the collected features, the CCT values from the submitted images are estimated using a unique Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA_BRNNet) model. The Coati Optimisation Algorithm (COA) is used to improve the performance of a recommended classifier by modifying its parameters. In the Result section, the suggested model is compared to various current techniques, obtaining an MAE value of 529K and an RMSE value of 587K, respectively