Dutch and Victorian approaches to land appraisal

Stiboka onderzoe

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

A radically emergentist approach to phonological features: implications for grammars

Phonological features are often assumed to be innate (Chomsky & Halle 1968) or learned as a prerequisite for learning grammar (Dresher 2013). In this paper, I show an alternative approach: features are learned in parallel with grammar. This allows for addressing an interesting question: is it really optimal that the phonological grammar only use phonological features to refer to segmental material (Chomsky & Halle 1968), or could it be more advantageous for the grammar to refer to segmental material on more than one level of representation? The learner considered here finds that it is only optimal for the grammar to use phonological features to refer to multiple segments in the same pattern (e.g., the class of nasals), but when a pattern refers to a single segment, it may be at least equally good for the grammar to refer to this single segment as a bare segment label (for instance, [m] instead of [labial, nasal]). In this way, the grammar uses different kinds of representational units (features and non-features) for the same sound – which mimics models with multiple layers of representation (such as Goldrick 2001, Boersma 2007)

Does Feature Reduction Help Improve the Classification Accuracy Rates? A Credit Scoring Case Using a German Data Set

The paper broadly discusses the data reduction and data transformation issues which are important tasks in the knowledge discovery process and data mining. In general, these activities improve the performance of predictive models. In particular, the paper investigates the effect of feature reduction on classification accuracy rates. A preliminary computer simulation performed on a German data set drawn from the credit scoring context shows mixed results. The six models built on the data set with four independent features perform generally worse than the models created on the same data set with all 20 input features.   &nbsp

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Classification Models for Symmetric Key Cryptosystem Identification

The present paper deals with the basic principle and theory behind prevalent classification models and their judicious application for symmetric key cryptosystem identification. These techniques have been implemented and verified on varieties of known and simulated data sets. After establishing the techniques the problems of cryptosystem identification have been addressed.Defence Science Journal, 2012, 62(1), pp.38-45, DOI:http://dx.doi.org/10.14429/dsj.62.144

Feature Selection Methods for Writer Identification: A Comparative Study

Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper feature selection methods are selected and their performances are analyzed using image dataset from IAM Handwriting Database. It is also analyzed the number of features selected and the accuracy of these methods, and then evaluated and compared each method on the basis of these measurements. The evaluation identifies the most interesting method to be further explored and adapted in the future works to fully compatible with Writer Identification domain