Application of Machine Learning in Cancer Research

This dissertation revisits the problem of five-year survivability predictions for breast cancer using machine learning tools. This work is distinguishable from the past experiments based on the size of the training data, the unbalanced distribution of data in minority and majority classes, and modified data cleaning procedures. These experiments are also based on the principles of TIDY data and reproducible research. In order to fine-tune the predictions, a set of experiments were run using naive Bayes, decision trees, and logistic regression. Of particular interest were strategies to improve the recall level for the minority class, as the cost of misclassification is prohibitive. One of The main contributions of this work is that logistic regression with the proper predictors and class weight gives the highest precision/recall level for the minority class. In regression modeling with large number of predictors, correlation among predictors is quite common, and the estimated model coefficients might not be very reliable. In these situations, the Variance Inflation Factor (VIF) and the Generalized Variance Inflation Factor (GVIF) are used to overcome the correlation problem. Our experiments are based on the Surveillance, Epidemiology, and End Results (SEER) database for the problem of survivability prediction. Some of the specific contributions of this thesis are: · Detailed process for data cleaning and binary classification of 338,596 breast cancer patients. · Computational approach for omitting predictors and categorical predictors based on VIF and GVIF. · Various applications of Synthetic Minority Over-sampling Techniques (SMOTE) to increase precision and recall. · An application of Edited Nearest Neighbor to obtain the highest F1-measure. In addition, this work provides precise algorithms and codes for determining class membership and execution of competing methods. These codes can facilitate the reproduction and extension of our work by other researchers

NON DESTRUCTIVE EVALUATION OF CHLORIDE IN CONCRETE

Bridge decks in the cold climate region of the country, which have snow part of the year, are exposed to deicer salt in order to overcome the public demand for safe pavements. The chloride content in the salt can penetrate into the concrete through hairline cracks or diffusion in the concrete. This can establish galvanic corrosion microcells, and ultimately damage the concrete and reduce the life performance of the structure through expansive forces created by corroded steel. The issue of chloride penetrating into concrete has been under study and research for a long time. There is a critical need in civil engineering for methods that can nondestructively measure the condition of existing reinforced concrete structures. The focus of this thesis is on a nondestructive prompt gamma neutron activation (PGNA) chloride detector. This technique is a specialized use of prompt gamma/neutron activation, a spectroscopic technique for elemental analysis of materials. The elements of PGNA are identified by characteristic gamma rays emitted from the target material while it is being bombarded with neutrons. The objective of this research is to design a test program for determining the calibration factor, which relates the detected chloride gamma ray counts to the actual chloride concentration in the concrete, and its uncertainties through the use of cast concrete samples with known chloride contents

Output Voltage Improvement of a Matrix Converter under Unbalance and Distorted Input Voltage Using PR Controller

Matrix converter is an AC to AC converter without any energy storing element in the dc-link; therefore, any distortion in the input voltage directly affects the output voltage quality. In this paper, firstly, space vector modulation for direct matrix converter is discussed. Afterwards, a closed loop method without output voltage sensors is proposed in order to reduce the distortions in the output voltage. In the proposed method, output currents are measured and compared with their reference values, then, the error goes into a proportional resonant (PR) controller to determine the modulation index and angle of the output voltage. No need for output voltage sensors, simple implementation and low computational burden can be considered as the advantages of the proposed method. Although the method is presented for direct matrix converter, it can be adopted easily for indirect matrix converters. To show the effectiveness of the proposed method, comprehensive simulation tests are conducted and the obtained results are compared with previously proposed method

Cost Effective, Highly Efficient Wireless Power Transfer Systems for EV Battery Charging

The impact of changing inner diameter of wireless power transfer (WPT) coils on coupling coefficient is studied. It is demonstrated that at a certain outer and inner coil diameter, turn space variation has minor effect on the coupling coefficient. Next, two compensation networks, namely primary LCC and secondary LCC, which offer load-independent voltage transfer ratio and zero voltage switching for WPT, are presented. For both compensation networks, the condition for having zero voltage switching operation are derived. In addition, load-independent voltage transfer ratio (VTR) frequencies are obtained and VTR at each frequency is derived. Then, required equations for calculation of WPT system efficiency based on its equivalent circuit are presented. Eventually, by defining a time-weighted transfer average efficiency (TWTAE), and based on measured values of resistance and inductance of a WPT prototype and experimental charging curve of a Li-ion battery, a design procedure for both compensation networks is proposed. The proposed design leads to high TWTAE as well as low material usage. Simulation and experimental results verify the superiority of proposed coil and compensation design compared to conventional one

Privacy-preserving Data Mining on Hospitality Big Data

In this paper we present a summary of our activity associated with the security and encryption the big data on the hotels big data. We give a brief introduction to some techniques for security the data set in large scale, we then look into the homomorphism and Map Reduce environment. With the advances in computer architecture and silicon technology, processing large data set becomes possible after some fundamental data storage and processing algorithm been proposed and implemented. Analyzing the big data opened many opportunities for scientists in different research and application areas. Hospitality industry, for example, collects and keeps customers information, which proposed some significant challenges to be addressed. The first challenge how to save and keep all these massive data set; and the second challenge is securing this sensitive information. In this paper we will discuss Parallel Homomorphic Encryption (PHE) security method which can be used in companies’ information storage, processing and management

A Survey on Recent Named Entity Recognition and Relation Classification Methods with Focus on Few-Shot Learning Approaches

Named entity recognition and relation classification are key stages for extracting information from unstructured text. Several natural language processing applications utilize the two tasks, such as information retrieval, knowledge graph construction and completion, question answering and other domain-specific applications, such as biomedical data mining. We present a survey of recent approaches in the two tasks with focus on few-shot learning approaches. Our work compares the main approaches followed in the two paradigms. Additionally, we report the latest metric scores in the two tasks with a structured analysis that considers the results in the few-shot learning scope

Effect of Tetracaine on Intraocular Pressure in Normal and Hypertensive Rabbit Eyes

Purpose: To evaluate the effect of tetracaine on intraocular pressure (IOP) in normal and hypertensive rabbit eyes. Methods: The study was conducted on 12 healthy rabbits as controls and 6 healthy rabbits in which an experimental model of ocular hypertension (OHT) was induced by administration of 70 mL/kg of tap water through an orogastric tube. One drop of tetracaine was instilled in the left eye while a drop of normal saline (placebo) was applied to the right eye of the control group. IOP was measured before and 0, 5, 10, 15, 20, 25, 30, 35 and 40 minutes after drop administration in this group. The OHT group also received one drop of tetracaine and normal saline in the left eyes and right eyes respectively, immediately after water loading; the instillation of drops was repeated after 55 minutes. IOP was measured before and 0, 5, 10, 15, 20, 25, 30, 35, 40, 55, 70, 85, 100 and 115 minutes after water loading in this group. Results: Tetracaine treated eyes in both groups (ocular hypertensive and normal controls) demonstrated significant IOP reduction at time zero (immediately after drop instillation) which was sustained up to 20 minutes, as compared to placebo treated eyes (P<0.05). In ocular hypertensive rabbits, repeat instillation of tetracaine significantly reduced IOP at 55 minutes up to 30 minutes thereafter. Conclusion: Topical tetracaine can reduce IOP; this fact should be considered in experiments evaluating IOP reducing agents

A cost benefit analysis of retrofitting public policies on Atlanta residential housing

The residential building sector has a major share in carbon emission and energy consumption. In the US, around 60% of the housing stock belongs to the owner-occupied sector. Since more than half of the existing building stock was built before the modern energy efficiency standards are taken place, there is a potential to reduce the energy consumption and greenhouse gas emissions in this sector, only by retrofitting the existing buildings. However, this goal cannot be achieved without a larger scale Cost Benefit Analysis (CBA) to develop and demonstrate market ready retrofit solutions/policies from both the government and the homeowner’s standings. To this extend, the aim of the presented paper is to conduct a city-level CBA on the city of Atlanta which ranked 5th in producing GHG emissions among 100 US metropolitan areas while residential buildings sector is ranked 4th among other contributing sectors. To this end, a hypothetical public policy of retrofitting single-family residential buildings built before 1970s is proposed with the objective of reducing the regional energy consumption rate while calculating the upper bound of the tax to be proposed on the properties rejecting to renovate. The preliminary results of this CBA revealed that although retrofitting all the prior 1970s buildings won’t be beneficial comparing to the status quo, the numbers are highly sensitive to the proposed discount rate as well as the percentage of the homeowners practically decide to retrofit. The sensitivity analysis showed that if only 30-40% of participants decided not to renovate and pay the tax, the CBA could be a positive Net Present Value (NPV) with a relatively low tax rate (less than $0.5/sqft) implementation. Therefore, it is recommended to more accurately study the reaction of the homeowners to the policy before implementing the tax/subsidy rates while precisely observe the fluctuations of the market discount rate

Improving Profitability of a Color Production Line by Breaking Down Bottlenecks: A Computer Simulation Approach

Bottlenecks are one of the controversial issues in manufacturing companies. Managers and designers attempt to manage this trouble to improve efficiency in different ways. For example, expanding capacity is a prevalent solution to get rid of bottlenecks. In this paper, a color production line is chosen, which faces several challenges in its production line. This company attempts to distinguish and diminish the bottlenecks in the production line. The objective of this paper is to build a developed model of a production line to improve its profitability by breaking down its bottlenecks. Besides, the optimum number of operators with different utilizations is investigated in this paper. Furthermore, we considered the construction of new quality control in the station, which is the most time-wasting operation in the production line. The current study aims to apply computer simulation to examine the production line bottlenecks. In doing so, arena 14.00 software is used. Then the results are analyzed, and several managerial implications are presented.Comment: 10 page

Assessment of fish farm effluents on macroinvertebrates based on biological indices in Tajan River (north Iran)

Impacts of effluent from fish farming activities on fluvial ecosystems lead to deterioration of water quality and changes in the macroinvertebrates assemblage. In this study, the influence of fish farm effluents on water quality and macroinvertebrates communities of Tajan River was investigated to evaluate the suitability of macroinvertebrates based on biological metrics and indices. Benthic macroinvertebrate communities were analyzed seasonally for a period of one year . Five sampling stations were selected along the study reach of 50 km. Station 1(S1) which is located upstream from the fish farm, was used as the reference site. Station S2 and S3 were located downstream from the fish farm outlet; S4 and S5 were further downstream. In order to assess the changes in diversity and richness in relation to water quality,two major groups of sites based on similarity between macroinvertebrate communities identified by cluster analysis. Diversity of macroinvertebrates, EPT richness and EPT/CHIR indices significantly decreased toward downstream stations except for station S4. Conversely, values of HFBI and Jacard index significantly increased in the downstream stations. The present study revealed significant differences in water quality parameters between the stations located above and below the fish farms. Owing to the relatively high diversity of benthic macroinvertebrates inhabiting rivers, use of macroinvertebrate based biological indices is recommended for assessment of water quality and pollution in fluvial systems