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

Attitude towards and use of ecstasy in medical university interns? based on HBM

Using a self-reported questionnaire, 130 Yazd Medical University students were surveyed about their knowledge towards ecstasy and their use of ecstasy based on Health Belief Model. The age range was 18-31 years. Approximately, 23% of students had seen an ecstasy tablet, 6 (4.6%) had used ecstasy (2 female and 4 male), 4 of them lived in a dormitory and 2 were tenants. The levels of knowledge, perceived barrier and perceived benefit of students who had used ecstasy were lower than those who hadn?t used ecstasy. There was a significant difference between the knowledge, perceived barrier and perceived benefit of samples and use of ecstasy (p inf. 0.008, p inf. 0.003 and p inf. 0.13, respectively). Approximately, 74% of the students were eager to know more about ecstasy and its effects. Finally, the students were asked to select one or more item from a list of six which they considered the best way for providing young people with accurate information, and the responses (as percentages) for each source were as follows: discussion with parents: 1.5%; television programs: 64.6%; radio programs: 1.5%; talk at university: 12.3%; friends: 12.3%; newspapers/magazine articles: 7.7%. The data revealed that the knowledge of participants about ecstasy was low (mean = 27.69 ± 3.53 out of 48).The mean grade score of knowledge of males was more than females. A survey in Kerman (Iran) showed that the knowledge of general practitioners about ecstasy was lower than 50% and the knowledge of males was more than females

Nano-Fe3O4/O2: Green, Magnetic and Reusable Catalytic System for the Synthesis of Benzimidazoles

Magnetic nano-Fe3O4 was applied in the presence of atmospheric air as a green, efficient, heterogeneous and reusable catalytic system for the synthesis of benzimidazoles via the reactions of o-phenylenediamine (1 eq) with aryl aldehydes (1 eq) in excellentyields (85–97 %) and short reaction times (30–100 min) with a proposed mechanism.Keywords: Benzimidazole, benzene-1,2-diamine, aldehyde, nano-Fe3O4, heterogeneous catalyst, magnetite, O

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

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?

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

Crystalline phases involved in the hydration of calcium silicate-based cements: Semi-quantitative Rietveld X-ray diffraction analysis

Chemical comparisons of powder and hydrated forms of calcium silicate cements (CSCs) and calculation of alterations in tricalcium silicate (Ca3SiO5) calcium hydroxide (Ca(OH)2) are essential for understanding their hydration processes. This study aimed to evaluate and compare these changes in ProRoot MTA, Biodentine and CEM cement. Powder and hydrated forms of tooth coloured ProRoot MTA, Biodentine and CEM cement were subjected to X-ray diffraction (XRD) analysis with Rietveld refinement to semi-quantitatively identify and quantify the main phases involved in their hydration process. Data were reported descriptively. Reduction in Ca3SiO5 and formation of Ca(OH)2 were seen after the hydration of ProRoot MTA and Biodentine; however, in the case of CEM cement, no reduction of Ca3SiO5 and no formation of Ca(OH)2 were detected. The highest percentages of amorphous phases were seen in Biodentine samples. Ettringite was detected in the hydrated forms of ProRoot MTA and CEM cement but not in Biodentine