Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises

In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?

Towards the Establishment of a Porcine Model to Study Human Amebiasis

BACKGROUND: Entamoeba histolytica is an important parasite of the human intestine. Its life cycle is monoxenous with two stages: (i) the trophozoite, growing in the intestine and (ii) the cyst corresponding to the dissemination stage. The trophozoite in the intestine can live as a commensal leading to asymptomatic infection or as a tissue invasive form producing mucosal ulcers and liver abscesses. There is no animal model mimicking the whole disease cycle. Most of the biological information on E. histolytica has been obtained from trophozoite adapted to axenic culture. The reproduction of intestinal amebiasis in an animal model is difficult while for liver amebiasis there are well-described rodent models. During this study, we worked on the assessment of pigs as a new potential model to study amebiasis. METHODOLOGY/PRINCIPAL FINDINGS: We first co-cultured trophozoites of E. histolytica with porcine colonic fragments and observed a disruption of the mucosal architecture. Then, we showed that outbred pigs can be used to reproduce some lesions associated with human amebiasis. A detailed analysis was performed using a washed closed-jejunal loops model. In loops inoculated with virulent amebas a severe acute ulcerative jejunitis was observed with large hemorrhagic lesions 14 days post-inoculation associated with the presence of the trophozoites in the depth of the mucosa in two out four animals. Furthermore, typical large sized hepatic abscesses were observed in the liver of one animal 7 days post-injection in the portal vein and the liver parenchyma. CONCLUSIONS: The pig model could help with simultaneously studying intestinal and extraintestinal lesion development

Expression patterns of angiogenic and lymphangiogenic factors in ductal breast carcinoma in situ

The objective of this study was to investigate expression of various growth factors associated with angiogenesis and lymphangiogenesis and of their receptors in ductal carcinomas in situ of the breast (DCIS). We studied protein expression of basic fibroblast growth factor (bFGF), vascular endothelial growth factor (VEGF)-A, endothelin (ET)-1, and VEGF-C, and their receptors bFGF-R1, Flt-1, KDR, ETAR, ETBR, and Flt-4 immunohistochemically in 200 DCIS (pure DCIS: n=96; DCIS adjacent to an invasive component: n=104) using self-constructed tissue microarrays. Basic fibroblast growth factor-R1, VEGF-C, Flt-4, and ETAR were expressed in the tumour cells in the majority of cases, whereas bFGF and Flt-1 expression was rarely observed. VEGF-A, KDR, ET-1, and ETBR were variably expressed. The findings of VEGF-C and its receptor Flt-4 as lymphangiogenic factors being expressed in tumour cells of nearly all DCIS lesions and the observed expression of various angiogenic growth factors in most DCIS suggest that in situ carcinomas are capable of inducing angiogenesis and lymphangiogenesis. Moreover, we found a higher angiogenic activity in pure DCIS as compared to DCIS with concomitant invasive carcinoma. This association of angiogenic factors with pure DCIS was considerably more pronounced in the subgroup of non-high-grade DCIS (n=103) as compared with high-grade DCIS (n=94). Determination of these angiogenic markers may therefore facilitate discrimination between biologically different subgroups of DCIS and could help to identify a particularly angiogenic subset with a potentially higher probability of recurrence or of progression to invasiveness. For these DCIS, targeting angiogenesis may represent a feasible therapeutic approach for prevention of progression of DCIS to invasion

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

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Measurement of top quark–antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

Peer reviewe

Measurement of prompt J/ψ pair production in pp collisions at √s = 7 Tev

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