7^th International Conference on Biostatistics and Bioinformatics September 26-27, 2018 Chicago, Illinois, USA

Biography:

Gadde Srinivasa Rao received his MSc in Statistics (1988), MPhil.in Statistics (1994) and PhD in Statistics (2002) from the Acharya Nagarjuna University, Guntur, India. He is presently working as Professor of Statistics at the Department of Statistics, The University of Dodoma, Tanzania. He boasts more than 90 publications in different peer-reviewed journals in national and international well reputed journals including for example, Journal of Applied Statistics, International Journal of Advanced Manufacturer Technology, Communications in Statistics-Theory and methods, Communications in Statistics-Simulation and Computation, Journal of Testing and Evaluation, Arabian Journal for Science and Engineering, International Journal of Quality & Reliability Management, Economic Quality Control and Journal of Statistical Computation and Simulation. He is the reviewer for various reputed international journals. His research interests include statistical inference, statistical process control, applied Statistics, acceptance sampling plans and reliability estimation.

Abstract:

The diabetes monitoring is very important to maintain the diabetic levels in control. The Shewhart control chart has been widely used in the healthcare department for the monitoring of sugar levels. In this paper, we will present a more efficient way to monitor diabetic level in diabetes patients. The structure of control is presented using repetitive sampling. The efficiency of the proposed chart in detecting a shift in diabetic level is compared with the existing chart. It is found that the proposed chart provides a strict way to monitor the diabetic levels in diabetes patients. So, the application of the proposed chart is shown using simulation study and real data collected from diabetes patient. It can be concluded that the use of the proposed chart in health care issue may reduce the risk of heart disease by monitoring diabetic levels in an effective way.

Biography:

Abdul Basit is the PhD Research Scholar in the discipline of Statistics in National College of Business Administration and Economics Lahore, Pakistan. He has completed his MS in Social Sciences from SZABIST Karachi, Pakistan in 2014. Currently, he is serving as Deputy Director in Research Cluster of State Bank of Pakistan. He has published 07 research papers in journals and many articles were presented at national and international conferences.

Abstract:

Forecasting of key economic indicators has an important role in the policymaking. Statisticians and economist are still trying to find out the techniques and models which provides a more accurate forecast. There are different time series models are available in the literature like Auto-Regressive (AR) model, Moving Average (MA) model, Auto-Regressive Moving Average (ARMA) model, Auto-Regressive Integrated Moving Average (ARIMA) model, Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model, and many others. ARIMA and ARFIMA mostly used for the analysis of time series. In this study, we are trying to estimate the differencing parameter’ using the information function and entropy. The comparison of classical time series models and a new time series model is also included in this study. The new estimator of the differencing parameter will give us a more accurate forecast as compared to the classical time series models.

Biography:

Sumith Gunasekera received the (Special Bachelor of Science) B.Sc.(Sp.) degree in Physics in 1995 from the University of Colombo, Colpetty, Colombo 03, District of Colombo, Western province, Democratic Socialist Republic of Sri Lanka (DSRSL) (formerly known as Ceilão (in Portuguese under their rule), Seylon (by Dutch under their rule), and Ceylon (by British under their rule), and the (Doctor of Philosophy) Ph.D. degree in Statistics in 2009 from the University of Nevada at Las Vegas (UNLV), Las Vegas, NV, United States of America (USA). Sumith joined the Department of Mathematics at The University of Tennessee at Chattanooga, Chattanooga TN, the USA in 2009, and has been an Associate Professor of Statistics since 2015. He is the author of many seminal statistical articles and is the recipient of several grants and awards. His research interests include statistical inference, reliability, survival analysis, the design of experiments under classical, Bayesian, and generalized frameworks.

Abstract:

It has become increasingly common in epidemiological studies to pool specimens across subjects as a useful cost-cutting technique to achieve accurate quantification of biomarkers and certain environmental chemicals. The data collected from these pooled samples can then be utilized to estimate the Youden Index (or Youden Statistic) developed by Youden (Youden WJ. Index for rating diagnostic tests. Cancer 1950;3(1):32– 35.), which measures biomarker’s effectiveness and aids in the selection of an optimal threshold value, as a summary measure of the Receiver Operating Characteristic (ROC) curve. The aim of this paper is to make use of generalized approach due to Tsui and Weerahandi ( Tsui K, Weerahandi, S. Generalized p-values in significance testing of hypotheses in the presence of nuisance parameters. J. Amer. Statist. Assoc. 1989;84(406):602–607.) to estimate and testing of the Youden index. This goal is accomplished by the comparison of classical and generalized procedures for the Youden Index with the aid of pooled samples from the shifted-exponentially distributed biomarkers for the low-risk and high-risk patients. These are juxtaposed using confidence intervals, p-values, the power of the test, the size of the test, and coverage probability with a wide-ranging simulation study featuring a selection of various scenarios. In order to demonstrate the advantages of the proposed generalized procedures over its classical counterpart, an illustrative example is discussed using the Duchenne Muscular Dystrophy (DMD) data available at
http://biostat.mc.vanderbilt.edu/wiki/Main/DataSets or http://lib.stat.cmu.edu/datasets/.

Biography:

Asif Ali has completed MS degree in Applied Economics in 2014. He has also sufficient knowledge in the discipline of Statistics with additional degree of MSc (Statistics). Currently he is working in Central Bank of Pakistan in the International Trade section.

Abstract:

Entropy, a mathematical tool used to gather maximum information regarding understudying distribution, systems, databases and surveys. We are introducing entropy as a tool which provides the maximum information about the trading behaviour in different regions. This study will lead us to explore the new avenues of business and investment in Pakistan. China is the biggest player in global trade from the Asian region. To expand the scope of competitiveness, China is continuously investing in the world. The China–Pakistan Economic Corridor (CPEC) is one of the major projects. An extension of China's economic ambition One Belt-One Road initiative (OBOR). In future, China wants to expand her trade with the world using the CPEC to enhance the scope of competitiveness. Pakistan also believes in open trade and continuously trying to enhance trade with the world. To attain maximum advantage of CPEC, to explore the opportunities for the investors and business communities. In this study, we will develop linkages between the trends of our industries, commodities and their future demand in different regions.

Biography:

Olawale Abolade O is a Lecturer in the Department of Statistics, Federal Polytechnic, Ile-Oluji, Ondo State, Nigeria. She holds an MSc Degree in Statistics. Mrs. Olawale is a member of many Professional bodies among which are International Biometry Society (IBS), Nigerian Statistical Association (NSA), Nigerian Mathematical Society (NMS). She has attended so many conferences, seminars and workshops both locally and abroad to update her knowledge in which she has presented some of her works. Presently, she is a PhD Student in the Department of Statistics, University of Ilorin, Nigeria. Her research area is Bio-statistics with a special interest in Survival Analysis.

Abstract:

A retrospective data set was obtained from a government University Teaching Hospital for breast cancer patients classified by the cause of death. The first cause of death was cancer while the other cause arose from any other, all of which were put together and referred to as the competing risk. Appropriate probability distributions were fitted to the time-to-death data of the two groups. The three-parameter Weibull distribution was appropriate to the patients who died of cancer, while exponential probability distribution fitted the breast cancer patients who died of other causes. The implication of this distribution is that survival chance changes between the two competing risks such that overtime other causes of death overshadows death arising from breast cancer.

Biography:

Tamer Elsayed has completed his PhD in the year 2014 from the faculty of Engineering Cairo University. His PhD entitled “Development of an artificial neural network to predict the concrete deterioration due to chemical attack”. He is a supervisor for many MSc students. He is a member of many research projects. He has published many papers. One of his published papers entitled “Evaluation of field concrete deterioration under real conditions of seawater attack”, Construction and Building Materials 119 (2016):130–144).  

Abstract:

In this study, a multi-gene genetic programming (MGGP) and artificial neural network (ANN) techniques are utilized to create two models for prediction of concrete uniaxial compressive strength. Concrete is a highly complicated heterogeneous material and a precise model of its uniaxial compressive strength is highly nonlinear. Due to the importance of concrete uniaxial compressive strength as the most important characteristic of concrete, converting gathered experimental data from literature to a user-friendly formula is strongly needed for concrete mix design purpose and consequently structural analysis applications. The proposed mathematical expression links the concrete ingredients such as water content, super-plasticizer content, cement content, fly ash content, etc., as inputs and uniaxial compressive strength as output. The results indicated that the created MGGP model and ANN model are precisely able to predict the concrete uniaxial compressive strength in close agreement with the experimental results. Finally, the process of formulation of mathematical equations utilized in this study is a useful guideline in data fitting applications.

Biography:

Ergi Sener, who is indicated as one of the 20 Turkish people to be followed in the field of technology, received a BS in Microelectronics Engineering in 2005 and double MS in Telecommunications & Management in 2007 from Sabanci University. He is pursuing a PHD degree in Technology Management & Innovation. He began his career as the co-founder and business development director of New Tone Technology Solutions in 2007 with the partnership of Sabancı University's Venture Program. Between 2009 and 2013, he worked as a CRM specialist at Garanti Payment Systems. In 2013, he joined MasterCard as a business development and innovation manager. He was also one of the co-founders and the managing director of Metamorfoz ICT, a new generation Fintech company and Bonbon Tech, the leader IoT focused new generation analytics company. He is currently acting as the Executive Board Member & CDO of a Dutch-based incubation center IdeaFiedl BV. During his career, along with many others he received “Global Telecoms Business Innovation Award" in 2014, "MasterCard Europe President's Award for Innovation" in 2013, "Payment System of the Year Award" by Payment Systems Magazine in 2012, and "Best Mobile Transaction Solution Award" by SIMagine in 2011.

Abstract:

IdeaField is a disruptive technology innovation that understands and analyses in-store customer behavior (wait time, service time, visit frequencies etc.) without being connected to any wi-fi, or open Bluetooth or without having a smart phone application. Data is collected from wi-fi mode-on mobile devices at the locations, where IdeaField sensors have been deployed. With its unique technology, IdeaField aims to perform real-time behavior-based analysis.

In this workshop, a detailed IdeaField demonstration will be shown with covering all use cases such as employee tracking, queue management, real-time heat maps, location based campaigns, convergence analytics, reporting and other consultancy cases. Also, competitive analysis based on other micro-location based technologies (such as cameras, beacons, sensors, etc) will be covered.

Biography:

Yongxiang Gao has been studying full-time in the graduate program for Master’s Degree on Epidemiology and Health Statistics in the School of Public Health, Sun Yat-sen University from September 2016 to now. The normal study period is three years.

Abstract:

Epilepsy is a chronic neurological disease characterized by epileptic seizures that affect approximately 50 million people worldwide. An electroencephalogram is the most dominant method to detect epileptic seizures, it contains information about brain activity. Therefore, an automatic diagnostic method needed to be proposed to help the doctor make the correct decision, many methods have also been developed during the past years but there is no unanimous opinion. In this work, a strategy has been proposed to differential EEG as normal, epileptic seizures and interictal. Maximal overlap wavelet transform was used to extract wavelet coefficients, five features (variance, Pearson correlation coefficient, Hoeffdings’D measure, Shannon entropy, interquartile range) were calculated from EEG and then input to the linear discriminant classifier for the classification purpose. Data were collected from the Department of Neurology, the Second Affiliated Hospital of Guangzhou Medical University containing 34 healthy people, 30 epileptic seizures patients, and 21 interictal patients. Here only db4 was used. The performance of classifiers was evaluated use leave-one-out cross-validation in terms of accuracy and auc. Results show that the accuracy of healthy and epileptic seizures is 1 and auc is 1. The accuracy of interictal and epileptic seizures is 92.16% and auc is 0.96. The method we proposed can extract information from EEG.