Islamic Work Ethic

Islamic work ethic (IWE) has been on the rise in the last 10 years and started to attract the attention of researchers. This study examines the development of IWE studies. In this direction, studies with the theme of Islamic work ethic were determined by examining the studies published in the Web of Science database. How IWE research has changed and developed was interpreted by the content analysis method. For that purpose, scientific publications were analyzed in terms of publication year, type of study, research design, sample type, sample size, data collection method, country where the study was carried out, variable types, antecedents and consequences of the IWE by descriptive review model When past studies were examined, it was seen that there was no study examining the literature on IWE in the context of content analysis. This study aims to fill this gap in the literature, to evaluate the situation of the IWE phenomenon in the literature, and to reveal which variables it is associated with.

Temperature-induced Multi-species Cohort Effects in Sympatric Snakes

In reptiles, reproductive maturity is often determined by size rather than age. Consequently, growth early in life may influence population dynamics through effects on generation time and survival to reproduction. Because reproductive phenology and pre- and post-natal growth are temperature-dependent, environmental conditions may induce multi-species cohort effects on body size in sympatric reptiles. I present evidence of this using ten years of neonatal size data for three sympatric viviparous snakes, Dekay’s Brownsnakes (Storeria dekayi), Red-bellied Snakes (S. occipitomaculata) and Common Gartersnakes (Thamnophis sirtalis). End-of-season neonatal size varied in parallel across species such that snout-vent length was 36-61% greater and mass was 65-223% greater in years when gestating females could achieve higher April-May (vs. June-July or August-September) operative temperatures. Thus, temperature had a larger impact during follicular enlargement and ovulation than during gestation or post-natal growth. Multi-species cohort effects like these may affect population dynamics and increase with climate change.

Machine Learning to Early Prediction of Chronic Kidney Disease: Using Imbalanced and Limited Size Data Sets (Preprint)

BACKGROUND Chronic kidney disease (CKD) is a worldwide public health problem, usually diagnosed in the late stages of the disease, increasing public health costs and mortality rates. The late diagnosis is even more critical in low- and middle-income countries due to the high poverty levels, many hard-to-reach locations, and sometimes lack/precarious primary care. Therefore, to alleviate these issues, investment in early prediction is necessary. OBJECTIVE The purpose of this study is to assist the early prediction of CKD, addressing problems related to imbalanced and limited-size data sets. METHODS To address our multi-class problem (low risk, medium risk, high risk, and very high risk), we used data from medical records of 60 Brazilians with or without a diagnosis of CKD, containing the following attributes: hypertension, diabetes mellitus, creatinine, urea, albuminuria, age, gender, and glomerular filtration rate. We used two approaches for oversampling: (1) manual augmentation with data validated by an experienced nephrologist and (2) automated augmentation with the synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and Borderline-SMOTE support vector machine. We implemented classification models based on such data sets and the algorithms: decision tree (DT), random forest, and multi-class AdaBoosted DTs. We also applied the overall local accuracy and local class accuracy methods for dynamic classifier selection; and the k-nearest oracles-union, k-nearest oracles-eliminate, and META-DES for dynamic ensemble selection. We analyzed the models' performances using the hold-out validation, multiple stratified cross-validation (CV), and nested CV. We also computed the importance of features using feature selection methods. RESULTS The best performance was achieved using the DT and multi-class AdaBoosted DTs classification models, oversampled with SMOTE, and validated with the multiple stratified CV and nested CV methods. The DT model presented the highest accuracy score (98.99%) for both multiple stratified CV and nested CV, followed by multi-class AdaBoosted DTs (97.99% and 98.00%), respectively. CONCLUSIONS The SMOTE and multiple stratified CV or nested CV methods provided reliable results for such an imbalanced and limited size data set. During CKD monitoring, based on the DT model, assuming the previous DM evaluation, the user only needs to perform two blood tests: creatinine and urea. Thus, the DT model can assist in designing systems for the early prediction of CKD, providing easy interpretation and cost reduction.

A Proposal for Clothing Size Recommendation System Using Chinese Online Shopping Malls: The New Era of Data

Research was conducted in this study to design data-based size recommendation and size coding systems specifically for online shopping malls, expecting to lighten the burden of holding excessive inventories often caused by the high return rate in these online malls. The recommendation system has been implemented focusing mainly on size extraction and recommendation functions along with a UI (user interface). For the former function, data are necessary to extract customers’ sizes and, for instance, the system to be used in China adopts their Chinese standard body size GB/T (Chinese national standard) considering that there are a variety of body types in their substantial population. The system shows the most similar size dataset among the body size GB/T dataset to the customer once he/she inputs his/her height and weight. Each GB/T data was entered after categorizing it according to the proportion between height and weight. For the latter function, size recommendation, size coding was performed first for all the clothes by the shop owner by entering individual size data. The clothes providing the most suitable fit for the customer are recommended by the selection of that which has the smallest deviation between coded clothes size and the customer body data after performing a series of comparative calculations. To validate the effectiveness of the extraction, a method that checks whether the difference between extracted size and the body size that has been measured remains within the error range of 4cm was used. The result showed there to be an approximate 88% matching rate for women and a slightly lower accuracy of 80% for men. Moreover, the error rate was relatively smaller for the upper half clothing such as shirts, jackets, and blouses or one-piece dresses. Such a result may have been generated since the GB/T data were actually the average data entered 10 years prior without categorizing nationalities, ages, and body types in detail. This research emphasized the necessity of a database containing a more segmented human body size data, which can be effective for extracting and recommending sizes more accurately as the latest ones continue to accumulate.

Linear and Nonlinear Controls of Wireline Logs on Automated Grain Size Estimation Using Machine Learning Approach

Wireline logs have been utilized to indirectly estimate various reservoir properties, such as porosity, permeability, saturation, cementation factor, and lithology. Attempts have been made to correlate Gamma-ray, density, neutron, spontaneous potential, and resistivity logs with lithology. The current approach to estimate grain size, the traditional core description, is time-consuming, labor-intensive, qualitative, and subjective. An alternative approach is essential given the utility of grain size in petrophysical characterization and identification of depositional environments. This paper proposes to fill the gap by studying the linear and nonlinear influences of wireline logs on reservoir rock grain size. We used the observed influences to develop and optimize respective linear and machine learning models to estimate reservoir rock grain size for a new well or targeted reservoir sections. The linear models comprised logistic regression and linear discriminant analysis while the machine learning method is random forest (RF). We will present the preliminary results comparing the linear and machine learning methods. We used anonymized wireline and archival core description datasets from nine wells in a clastic reservoir. Seven wells were used to train the models and the remaining two to test their classification performance. The grain size-types range from clay to granules. While sedimentologists have used gamma-ray logs to guide grain size qualification, the RF model recommended sonic, neutron, and density logs as having the most significant grain size in the nonlinear domain. The comparative results of the models' performance comparison showed that considering the subjectivity and bias associated with the visual core description approach, the RF model gave up to an 89% correct classification rate. This suggested looking beyond the linear influences of the wireline logs on reservoir rock grain size. The apparent relative stability of the RF model compared to the linear ones also confirms the feasibility of the machine learning approach. This is an acceptable and promising result. Future research will focus on conducting more rigorous quality checks on the grain size data, possibly introduce more heterogeneity, and explore more advanced algorithms. This will help to address the uncertainty in the grain size data more effectively and improve the models performance. The outcome of this study will reduce the limitations in the traditional core description and may eventually reduce the need for extensive core description processes.

BIGDATA AND ITS APPLICATIONS

Technology has changed a lot in this digital era. Earlier we had landline phones but now wehave Smartphones, Laptops and Tablets that are making our life smarter. We were usingbulky desktops for processing huge amounts of data, we were using floppies and hard disksto store the data earlier. Now we can store data in the cloud. Due to the enhancement oftechnologyweweregeneratingalotofdata,forexampleeachsmartphoneuserapproximately generates 40 Exabyte’s of data every month in the form of texts, emails,phone calls, videos, photos, searches, music, etc., if this number is multiplied by 5 billionsmartphone users, that is a large amount. Traditional computing systems cannot handle thislarge amount of data. You have no idea about how much data you are generating in eachminute.Butthechallengingparthereisthatthedataisnotpresentinastructuredmannerand it is huge in size. Data is being generated in millions of ways and it is one of the biggestfactors for the evolution of Big Data. With the exponential growth of the data, people startedto store it in relational database systems. But with the advancements in the internet anddigitalization, they are insufficient. In order to overcome this, big data came into the picture.This Big Data.Provides a new set of tools and technologies to store a large amount ofunstructured data. Industryinfluencers,academicians,and other prominent stakeholders agree that Big Datahas become a big game-changer in most industries. The primary goal for most organizationsis to enhance customer experience, cost reduction, better-targeted marketing, and makingexistingprocessesmoreefficient.Inthispaper,welookintothevariousapplicationsthatBig Data offers to the industries, Industry-specific challenges that these industries face, andhow Big Data solves these challenges.