Search

In the application of decision-making systems in the field of healthcare, with advancing technology, the outputs of direct analysis devices have become usable. As the dataset becomes richer, the accuracy of models also increases. The parameters of the dataset used in this study contain raw data closer to real conditions in terms of both quantity and quality compared to previous studies. When examining the models established to identify liver diseases, it is observed that besides the model performance, the performance of experts also affects due to the use of parameters containing expert opinions. The data set used in this study did not include subjective data other than class values, and only expert opinions were used in training the model. Thus, the model performance will be less dependent on the dataset compared to other studies. Real-life data has been worked on with different models to see which structures are better. Artificial neural networks and particle swarm optimization methods were trained to solve the classification problem and results were analyzed by testing with training and test data in the study. [ABSTRACT FROM AUTHOR]

Pistachio nuts are a type of nut that is widely consumed around the world due to their high nutritional value and pleasant taste. Pistachios are usually sold in their shells, either open or closed. However, closed-shell pistachios are not well received by consumers, resulting in a lower commercial value. It is essential to be able to distinguish between open and closed pistachio shells in order to ensure quality control during production processes and processing. This can be done manually or by using mechanical devices. Manual inspection and categorization of pistachio nuts have traditionally been done by workers, but this process is inefficient in terms of time and money. Mechanical separation of open and closed-shell pistachio can damage the kernels of open-shell nuts due to the needle mechanism used in the sorting process. This study aims to classify pistachio nuts using a machine visionbased system and evaluate its applicability in terms of classification accuracy. The system is evaluated on the Antep pistachio species, which can be distinguished from other pistachio varieties, such as Siirt and Urfa pistachios, based on their shape, size, and taste properties. The machine vision system in this study classifies pistachio nuts into closed and open shell classes in a completely automated manner. In this study, 1,000 Antep pistachio nuts images were obtained and examined, including 500 open and 500 closed nuts. The images were pre-processed and prepared for feature extraction. From the images, a total of 14 color features were extracted. Although the single feature was used, promising classification accuracy rates of 95.6%, 94.8%, and 93.6% from the Random Forest, Support Vector Machine (SVM), and Logistic Regression were achieved, respectively. The performances of classifiers were compared to each other. Almost similar performances were detected. These results demonstrate that the Random Forest classifier is the most effective algorithm for classifying open and closed Antep pistachio nuts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Business Administration & Social Studies is the property of Aves Yayincilik Ltd. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anadolu (1300-0225) is the property of Anadolu Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi is the property of Journal of Economic Policy Researches / Iktisat Politikasi Arastirmalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tiroit kanseri, 2020'deki sonuçlara göre, tüm kanserlerin küresel insidansının %3'üne karşılık gelirken bazı ülkelerde son 30 yılda önemli ölçüde artmıştır. Tiroit nodülü, tiroit bezinin içinde bulunan bir lezyondur. Bu lezyonların kanserli olma olasılığı önemli bir endişe kaynağıdır. USG ile saptanan nodüller 1 cm'den büyük ve kötü huylu olma (Malignant) açısından kuşkuluysa, ince iğne aspirasyon (İİA) biyopsisi kullanılır ve değerlendirmeler yapılır. İyi huylu İİA sonuçları, gereksiz tiroit ameliyatlarının önlenmesine yardımcı olur. Kötü huylu (Malign) hücreler tespit edilirse, İİA sonucu cerrahi stratejinin belirlenmesinde etkin bir faktör olur. Buna rağmen, cerrahlar kötü huylu hücre potansiyeline ilişkin belirsizlik nedeniyle, çok yüksek oranda iyi huylu (Benign) tiroit dokusu rezeke etmektedirler. Bu nedenle, daha doğru sonuçlar veren ve cerrahi işlem gerektirmeyen (non-invasive) tekniklere ihtiyaç duyulmaktadır. Bu çalışmanın amacı, tiroit dokusu çok fazla rezeke edilmeden, hastanın verileri üzerinden makine öğrenmesi metotlarından biri olan Lojistik regresyon kullanarak, kesine yakın tanının elde edilmesidir. Bu çalışma ile hastaların test sonuçlarını kullanarak, nodülün kötü huylu (kanserli) olup olmadığını tahmin eden bir model üzerinde denemeler yaptık. Gerçekte kanserli olmadığı halde operasyon geçiren hasta sayısı üzerinde spesifik olarak yoğunlaşarak özgüllük (specificity) analizi yaptık. Lojistik regresyon sınıflandırma algoritması ile elde edilen sonuçlar içerisinden en iyi spesifiklik/özgüllük değerini %99,31 olarak elde ettik. While thyroid cancer accounts for 3% of the global incidence of all cancers, according to results in 2020, it has increased significantly in some countries over the last 30 years. The possibility that the thyroid nodule is cancerous is a significant concern. If nodules detected by USG are larger than 1 cm and are suspicious for malignancy, they are evaluated with fine needle aspiration (FNA) biopsy. Benign FNA results help prevent thyroid surgeries. If malignant cells are detected, it becomes an effective factor in the surgical decision. Surgeons resect a very high percentage of benign thyroid tissue due to uncertainty regarding the potential for malignant cells. Therefore, techniques that provide more accurate results and do not require surgical procedures are needed. The aim of this study is to obtain a near-definitive diagnosis by using Logistic regression, one of the machine learning methods, on the patient's data, without resecting the thyroid tissue too much. In this study, we experimented with a model that predicts whether the nodule is malignant (cancerous) or not, using the test results of the patients. We conducted a specificity analysis by focusing specifically on the number of patients who underwent surgery even though they did not have cancer. Among the results obtained with the logistic regression classification algorithm, we obtained the best specificity value of 99.31%. [ABSTRACT FROM AUTHOR]

Capacity vehicle routing problem (CVRP) is a very common problem in the cargo and logistics industry today. In these days we live in the era of big data, with the increasing need, logistics companies are faced with data that the number of nodes to be served is high and their locations are constantly changing. Therefore, this situation is challenging for existing solution techniques. In this study, the success of using machine learning techniques and classical operations research techniques together on solutions for CVRP solution was investigated. For this purpose, a two-stage approach which hybridizes machine learning techniques and mathematical programming formulations is proposed. In the first stage, it was decided the nodes to be assigned to which vehicles via machine learning algorithms, then it is ensured that the resulting clusters' total demand amount do not exceed the vehicle capacity with a method, which is called capacity balancing algorithm. In the second stage, the vehicle starts from the depot and visits all the assigned nodes to find the shortest travelled distance by using the traveling salesman problem (TSP) mathematical model. The machine learning algorithms that are used in this study are for supervised learning category; K-Nearest Neighborhood (K-NN) and logistic regression (LR) algorithms and for unsupervised learning category; K-Means algorithm. In order to analyze the applicability of the proposed hybrid methods to today's ever-changing conditions, the models under the supervised learning category were run on a dataset that were not seen during the training phase. For the proposed approaches, sensitivity analyzes were performed using datasets with different characteristics and dimensions from the literature, with different number of vehicles. And it has been shown that these hybrid approaches produce better results compared to CVRP GUROBI solutions and Large Neighborhood Algorithm on some test problems. [ABSTRACT FROM AUTHOR]

Copyright of SPORMETRE: The Journal of Physical Education & Sport Sciences / Beden Eğitimi ve Spor Bilimleri Dergisi is the property of SPORMETRE: The Journal of Physical Education & Sport Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The research aims to determine whether ESG scores, reflecting environmental, social, and corporate governance performances, influence the opinions provided by independent auditors for the financial statements of companies continuously traded on Borsa Istanbul (BIST) between 2018 and 2022. The study considers ESG performance as an independent variable, while business size, net profit for the period, cash flows from main activities/banking activities, and leverage ratio are considered as control variables. The logistic regression analysis method was chosen to measure the relationships between the variables. The study reveals that businesses with high ESG performance are less likely to engage in fraudulent or misleading financial reporting compared to businesses with low ESG performance. [ABSTRACT FROM AUTHOR]

Orta Doğu coğrafyası dünyanın çatışmalarla parçalanmış tek bölgesi olmamasına rağmen, uzun zamandır dünyanın en çatışmalı bölgelerinden biri olarak kabul edilmektedir. Çatışmalar ve savaşlar, Orta Doğu’nun bölgesel sistemini derinden etkilemiştir. Çatışmalar ve savaşların olumsuz etkileri, devletler arası askeri anlaşmazlıkların nedenini anlamayı oldukça gerekli kılmaktadır. Devletler arası askeri anlaşmazlıklara yönelik tarihsel veri setlerinin varlığı ise askeri anlaşmazlıkların nicel yöntemlerle modellenmesini sağlamaktadır. Bu amaçla çalışmada Marwala tarafından kapsamlı bir şekilde incelenen ve açıklanan yedi bağımsız değişken (ittifak, komşuluk, uzaklık, büyük güç, kapasite, demokrasi ve ekonomik bağımlılık) ile Orta Doğu’daki devletler arası askeri anlaşmazlıklar, lojistik regresyon analizi ile modellenmiştir. Nicel analiz sonuçlarına göre bu yedi değişkenden en çok ekonomik bağımlılık faktörü (negatif yönde) ve komşuluk faktörünün (pozitif yönde) Orta Doğu devletlerinin askeri anlaşmazlığı ile ilişkili olduğu anlaşılmıştır. Diğer bir ifadeyle, Orta Doğu devletlerinin ekonomik açıdan bağımlı olduğu devletler ile daha az ve komşularıyla daha fazla askeri anlaşmazlık yaşadığı anlaşılmıştır.

Copyright of Journal of Emerging Economies & Policy is the property of JOEEP: Journal of Emerging Economies & Policy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A low-cost, easy-to-use e-nose is developed to detect the spoilage of ground meat. E-nose consists of hardware, software and data processing components. The main elements of hardware component are gas sensors sensitive to hydrogen sulfide (H2S) and ammonia (NH3). Using MIT App Inventor 2 an Android application is developed to run the hardware component, retrieve the data, preprocess and send it to Google Sheets. Classification model is developed, and data management is carried out in Google Colab and Google Script. Logistic regression method is used to develop classification models from the collected signals. The model classified the samples as "spoiled" and "fresh" based on the gas concentrations. The Nessler solution is used to determine the actual spoilage state. Ground beef samples stored in the refrigerator and at room temperature are used to obtain spoiled and fresh samples to develop a logistic regression model. A total of 36 samples are used to develop model. Another set of 24 samples is used to test model and prototype device performance. It is observed that all samples used in the testing phase were classified correctly. The cost of the system has been determined as approximately $100 considering January 2021 exchange rates.

Globalleşen dünyada, firmaların temel amaçları piyasa değeri maksimizasyonunu sağlamak ve finansal başarılarını devam ettirmektedir. Firmalar artan rekabet koşulları ve krizler karşısında piyasadaki varlıklarını devam ettiremedikleri takdirde finansal başarısızlık ile karşı karşıya kalmaktadırlar. Çalışmada, Borsa İstanbul A.Ş. (BIST)’de işlem gören toptan ve perakende ticaret sektöründeki şirketlerin (toptan 10; perakende 13) 2017-2021 dönemine ait yıllık finansal tabloları ve açıklamaları kullanılarak finansal başarısızlık tahmini yapılması amaçlanmıştır. Yapılan Lojistik Regresyon analizi sonuçlarına göre 3 yıl için başarılı tahmin oranı % 86.7 ile % 93.8 oranları arasında değişmektedir. Kullanılan modellerin doğru sınıflama başarılarını göz önüne alındığında, lojistik regresyon modeli tahminlerinin işletme finansal başarı ya da başarısızlığını önceden tahmininde iyi bir araç olduğu görülmektedir.

Bu araştırmada, Adıyaman ve Gaziantep illerinde organik tarım yapan üreticilerin tarım sigortasına yaklaşımları anket yoluyla belirlenmeye çalışılmıştır. Bu amaçla, Gaziantep ilinde 84, Adıyaman ilinde ise 79 adet organik tarım üreticisi ile yüz yüze anket yapılmıştır. Çalışmada, elde edilen verilerin değerlendirilmesinde ortalamalar ve Khi-kare yöntemi kullanılmıştır. Tarım sigortası yaptırıp yaptırmama durumunu etkileyen değişkenler lojistik regresyon analizi yapılarak incelenmiştir. Her iki ilde en çok sigorta yaptırılan organik ürün antepfıstığı olup, bunu zeytin, üzüm, ceviz, badem ve nar takip etmiştir. En çok sigorta yaptıran yaş grubunun 46 yaş üzeri ve ilköğretim mezun grupları olduğu görülmüştür. Adıyaman ilindeki organik tarım yapan çiftçilerin, tarım sigortası yaptırma durumunun Gaziantep ilindeki çiftçilere oranla 0,501 kat olduğu saptanmıştır. Üreticilerin tarım sigortası yaptırma nedeni olarak, ürünlerinin çok zarar görmesi, geleceğe daha güvenle bakabilmek, sürekli afet riskinin olması ve sigortaya devletin destek vermesini sebep olarak ifade etmişlerdir. Tarım sigortası yaptırmayan üreticiler gerekçe olarak, sigorta maliyetinin yüksek olması, hasar ödemelerinin zamanında yapılacağına inanmamaları ve doğal afetlerden hiç zarar görmemiş olmalarını bildirmişlerdir.

Occupational accidents in the construction industry occur more frequently when compared with other industries. Construction occupational accidents still have not been prevented at the desired level. Several studies in the literature have been conducted to predict the occurrence frequency of these accidents using classical statistical and machine-learning techniques. However, some challenges regarding imbalanced and multicollinearity problems present in the dataset are not considered while analyzing data with a large size and a large number of categorical variables. This study aims to predict the severity of nonfatal construction accidents considering mentioned challenges to obtain more accurate results. In this study, standard binary logistic regression, Firth, Ridge, Lasso, and Elastic Net Regularized logistic regression models were used for the prediction of lost workdays in the construction industry and results were compared. The data used were classified into five groups: victim, workplace, accident time, accident and sequence of events, and postaccident state-related variables. The results showed that Firth's logistic model is the best-performing model and age, education, vocational education, workplace size, project type, working environment, accident month and year, general and specific activities, material agent, type of injury, and part of body injured are the most significant variables. This study, by providing interpretable machine learning tools, is the first attempt to use proposed models in the area of construction safety in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Middle Eastern Studies / Türkiye Ortadoğu Çalışmaları Dergisi is the property of Sakarya University, Middle East Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study was conducted to predict independent audit opinions using the artificial neural network and the logistic regression methods. In this context, the financial statements and audit reports of the companies in Borsa Istanbul Chemicals, Petroleum, Rubber, and Plastic Products sector for the period of 2010-2020 were discussed. They showed a correct classification performance of 96.5% in the classification estimation made by the artificial neural network method and 94.3% in the classification estimation made by the logistic regression method. According to the results of the research, it was determined that the artificial neural network model revealed higher classification prediction. It is envisaged that the models discussed within the scope of the study can be used as an auxiliary tool to support decisions of independent auditors, internal auditors, managements, partners, investors, foreign resource providers, employees, commercial relations, regulatory public institutions, consultancy institutions, financial analysts and the public in audit planning, risk assessment, and quality control studies. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Studies - Economics, Finance, Politics is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Erzincan University Journal of Education Faculty / Erzincan Üniversitesi Egitim Fakültesi Dergisi is the property of Erzincan University Faculty of Education Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The automotive sector has a large share of the global economy. In recent years, sudden and sharp increases in the exchange rate in Türkiye have also caused automobile prices to soar. Many factors cause the prices of second-hand automobiles to increase. The study aims to investigate the existence of asymmetric information and the factors affecting the prices of secondhand automobiles in Türkiye. There is a large body of research on the macroeconomic factors affecting second-hand automobile prices. However, the study examines the micro factors affecting automobile prices. The sample consists of 6,262 second-hand automobiles in Türkiye. The model consisting of many independent variables (brand, type of transmission, fuel type, ownership status, additional equipment information, damage information, etc.) is analyzed using multiple and logistic regression. The results show that brand and traction type are the two most important factors affecting the prices of second-hand automobiles in Türkiye. However, damage and additional equipment information and ownership status do not affect the prices of second-hand automobiles. Asymmetric information exists in the automobile markets in Turkey. [ABSTRACT FROM AUTHOR]

The aim of the study is to reveal that different financial characteristics can be obtained when the cash flow profiles obtained as a result of examining the positive and negative aspects of the cash flows in the cash flow statement components are used together with the financial ratio analysis technique. In the study, first of all, cash flow profiles of 70 enterprises operating in the BIST manufacturing industry sector are determined by using the cash flow table data of the years 2012-2018. Afterwards using the data in the balance sheet and income statement of each business, the financial ratio results by years are determined. As a result, the relationship between profile features and financial ratio features is analyzed according to the panel logit analysis method of profile 2, profile 4 and profile 6, which exceeded 10%, within a total of eight profiles and a total of 490 observations. According to the results of the analysis, profile 2 is positively related to asset turnover and return on assets, while asset growth rate is negatively related, profile 4 is positively related to asset growth rate, stocks to assets ratio and equity growth ratio is negatively related to profile 6, and stocks to assets are negatively related to profile 2. It is determined that the rate of asset growth and the rate of asset growth are positively correlated, while the current rate, leverage rate and return on assets rate are negatively related. [ABSTRACT FROM AUTHOR]

The occurrence of Armillaria mellea (Vahl) P. Kumm. and the ecological characteristics of this fungus were studied in Kocaeli, Yuvacik dam basin mixed-broad leaved forests. During the surveys, we analyzed the sporocarps (fruiting bodies) of A. mellea growing up on woody plants in plots selected by cluster sampling in the Yuvacik dam watershed dominated by broad-leaved forests. The Runs test results showed that randomness rules complied in the selection of the plots, and there was no tendency (p= 0.109 > 0.05, z= -1.603). The presence/absence of A. mellea and environmental variables were tested with Chi-square analysis, and the temperature differed among these environmental variables. To the dendrogram, A. mellea was mainly seen in the south of the study area and preferred western aspects. It is understood that this macrofungus prefers the south of the study area because of the altitude. Our data showed that sporocarps of A. mellea generally occurred in the western aspect, at temperatures of 15-20°C, >80% humidity and 800-1000 m altitude. Our logistic regression analysis model (z=-9.508+0.307×temperature+0.081×humidity) showed that if the temperature and humidity change by 1 unit in the region, sporocarp formation is affected by 36% and 8.4%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Economics, Business & Politics (UEIP) is the property of International Journal of Economics, Business & Politics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cashew is one of the major commercial commodities contributing to the national economy of Tanzania as foreign revenue. And yet still the processing of cashew is run locally using manual labour for a big part. If processed well under ideal conditions, cashews kernels are expected to be white in colour. But due to various factors like prolonged roasting in the steam chambers or over-drying, some cashew kernels tend to have a slight brown colour, and these are referred to as scorched cashews. Despite sharing the same characteristics with white cashew kernels, including nutritional quality, these cashew kernels are supposed to be graded differently. In many places around the world, particularly in Tanzania, the sorting and grading process of cashew kernels is performed by hand. In international trade, cashew grading is very important and this means more effective and consistent methods need to be applied in this stage of production in order to increase the quality of the products. The objective of this study was to evaluate the use of traditional Machine Learning techniques in the classification of cashew kernels as white or scorched by using colour features. In this experiment, various colour features were extracted from the images. The extracted features include the means (µ), standard deviations (σ), and skewness (γ) of the channels in RGB and HSV colour spaces. The relevant features for this classification problem were selected by applying the wrapper approach using the Boruta Library in Python, and the irrelevant ones were removed. 5 models are studied and their efficiencies analysed. The studied models are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbour. The Decision Tree model recorded the least accuracy of 98.4%. The maximum accuracy of 99.8% was obtained in the Random Forest model with 100 trees. Due to simplicity in application and high accuracy, the Random Forest is recommended as the best model from this study. [ABSTRACT FROM AUTHOR]

In recent years, it has been observed that the dividend payout made by the share repurchases method has increased significantly in practice. This study aims to research the determinants of share repurchases of firms. Therefore, the sample of the study consists of a total of 161,592 observations and the data of 22,445 firms operating in 62 countries around the world between 2009-2019. The created model has been analyzed by logistic regression and the ordinary least squares test method. The independent variables used in the study consist of firm-level and country-level variables such as profitability, leverage, asset tangibility, size, growth opportunities, research and development expenditure rates, gross domestic product, foreign direct investment, and domestic savings rate. Also, countries are classified as countries that have adopted common law and civil law. According to the obtained results, the effect of foreign direct investment rate has no statistically significant on share repurchases according to both methods. Besides, it has been determined that other variables both at the firm-level and at the country level, have statistically significant and considerable effects on the dividend distribution of the firms by the share repurchases method. In addition, in researching the determinants of share repurchases, it has been concluded that the obtained findings by the logistic regression method reveal more consistent results with the theoretical expectations and previous evidence in the literature. The developed model within the scope of share repurchases decisions shows the strong and original side of the study. Consequently, the results of this study are thought to be a guide that gives a new perspective and contributes to the literature, practitioners, and investors. [ABSTRACT FROM AUTHOR]

Copyright of Ege Üniversitesi Ziraat Fakültesi Dergisi is the property of Ege Universitesi, Ziraat Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mediterranean Journal of Sport Science (MJSS) is the property of Mediterranean Journal of Sport Science (MJSS) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bartin University Journal of Faculty of Education is the property of Bartin University Journal of Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Uludag University Journal of the Faculty of Engineering (UUJFE) is the property of Uludag Universitesi, Muhendislik Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study was to investigate the effect of the auditor rotation application in Turkey on audit quality. In the study in which the independent auditor's modified opinion is used as the audit quality indicator, data retrieved for 138 companies operating in Borsa Istanbul in 2018 were analyzed using the penalized logistic regression and logistic regression methods. According to the research, it is concluded that mandatory and voluntary audit partner rotation improves the quality of independent audit. [ABSTRACT FROM AUTHOR]

This study aims to determine the characteristics of young farmers and their businesses that benefit from and cannot benefit from young farmer support in the Mediterranean Region and determine the factors that affect the benefit of young farmer project support. In 2016, a survey was conducted with all 160 producers who benefited from young farmer support, and a survey was conducted with 56 producers who applied for young farmer project support but could not benefit from it to make comparisons between groups. The tendency of farmers to benefit from the young farmer support project was determined using artificial neural networks and logistic regression analysis. It was determined that the majority of the producers who received support only made animal production and mixed production (livetock production and vegetable production), while the majority of the producers who did not receive support made only plant production. With both analysis methods, it was determined that the most critical variables that affect the benefit of young farmer project support are the type of activity, the share of non-agricultural income in total income, the number of farmers in the family, the education period, the status of having non-agricultural income and family size. The total correct classification rate was found to be 87.04% in the logistic regression analysis and 91.20% in the artificial neural network analysis, and it was seen that the classification percentages obtained by both methods were quite close to each other.

Copyright of Firat Universitesi Sağlik Bilimleri Tip Dergisi is the property of Firat Universitesiu, Saglik Bilimleri Enstitusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Yüksek kaldıraç noktası, x uzayının merkezine uzak olan değer olarak adlandırılır. İyi ya da kötü kaldıraç noktaları yüksek kaldıraç noktası olabilir. Kötü kaldıraç noktaları, yanlış sınıflandırılmış gözlemler veya x uzayındaki diğer gözlem değerleri ile uyumsuzluk gösteren aykırı değerlerdir. Kötü kaldıraç noktalarının belirlenmesinde maskeleme ve süpürme problemini ortadan kaldırmak için kullanılan grup silme yöntemi lojistik regresyon modelinde de kullanılmaktadır. Bu çalışmada kötü kaldıraç noktalarının belirlenmesinde literatürde mevcut olan Sapma Bileşenleri (Deviance Component, DEVC) yöntemi için bazı sağlam eşik değerleri önerilmiştir. Yapılan simülasyon çalışması ile sapma bileşenleri yönteminde kullanılması için önerilen sağlam eşik değerlerin literatürde mevcut olan eşik değerden daha iyi sonuçlar verdiği ortaya konmuştur.

The aim of this study is to determine the factors that influence financially successful and unsuccessful businesses. As part of the study, financial data of 48 businesses included in the BIST 100 index for the 2001-2019 period were tested by logistic regression analysis. Businesses that were financially successful and financially unsuccessful were determined by the Altman Z-Score model criteria. Based on the price detection reports published in the companies' IPO, independent audited balance sheets and income statements for the pre-IPO periods were used, as well as data for the stock transaction period. The relevant data is accessed through the Public Disclosure Platform, Finnet and İstanbul Stock Exchange. As a result of the analysis, it has been determined that the variables that increase the success of the firm are the current ratio, asset turnover, fixed asset turnover and profit before interest and tax / total assets ratio. The variables that decreased operating success were found to be the total leverage ratio, cash conversion duration and return on equity. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Itobiad: Journal of the Human & Social Science Researches / İnsan ve Toplum Bilimleri Araştırmaları Dergisi is the property of Itobiad: Journal of the Human & Social Science Researches and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of COMU Journal of Agriculture Faculty / ÇOMÜ Ziraat Fakültesi Dergisi is the property of Canakkale Onsekiz Mart University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: This study aims to compare the accuracy, reliability, and validity levels of the techniques by using various performance measures applying logistic regression models based on regularization approaches from data mining classification techniques on a dataset. Material and Methods: With the development of computerization and technology, machine learning is used in many fields as well as in the field of medicine. It has grown in popularity, particularly in cancer diagnosis. A urine biomarkers dataset from the public platform Kaggle database, which is freely available to all researchers, was used to reveal the most appropriate model for diagnosing patients' pancreatic ductal adenocarcinoma (PDAC). Because of the multicollinearity, the following regression models were considered to classify the disease diagnosis: Logistic lasso, logistic ridge, logistic elastic net, logistic adaptive lasso, logistic adaptive elastic net, and logistic adaptive group lasso. The classification success of the methods used was compared using reliability and validity criteria. Results: There were three statistically significant variables in all logistic regularization models, according to PDAC diagnostic results. Compared to the estimated model results, the logistic adaptive group lasso regression model appears to perform better in PDAC diagnosis. In addition to the three variables in this model, the variables age and plasma CA19-19 have been identified as important variables in PDAC diagnosis. Conclusion: As a result of comparative analyses, the logistic adaptive group lasso regression model outperformed the others in terms of performance measures. [ABSTRACT FROM AUTHOR]

Objective: Biologic disease-modifying antirheumatic drugs (bDMARDs) offer promising results for rheumatoid arthritis (RA) patients in general, but a substantial percentage of patients do not respond to them. It is important to predict the response before the treatment so that unnecessary adversities for the patients and costs for the healthcare system can be avoided. This study aims to develop a machine learning (ML) model that works with readily-available demographic and clinical factors for prediction of response to bDMARDs, and discusses additional non-pharmacological practices. Methods: Several ML models were tested in 190 RA patients from Turkey, and the logistic regression model was found to be superior. The relation between long-term and short-term responses were also analyzed. Results: Predictors of the logistic regression model were age, sex, coronary artery disease, spine surgery, steroid treatment, sulfasalazine treatment and baseline health assesment questionnaire score. The model displayed 79.5% accuracy and an area under receiver operating characteristic curve of 0.82. 87% of the patients who were goodresponders in six-month follow-up were also good responders in oneyear follow-up. Among non-responders in six-month follow-up, 75% were also non-responders in one-year follow-up. Conclusion: Making the prediction at an early stage is crucial for the patients as well as the healthcare system. However, it is equally important to determine how to proceed with the patients who are unlikely to respond to bDMARDs. Current literature does not adequately answer this question. Additional treatment options and multiple evaluation criteria for these options should be considered; multiple criteria models can provide useful decision support for this purpose. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Social Sciences / Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi is the property of Afyon Kocatepe University (AKU) Sosyal Bilimler Enstitusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Economic & Social Research is the property of Abant Izzet Baysal University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study was aimed at identifying the factors contributing to truck driver’s involvement in an injury accident in Kano, Nigeria using Logistic regression. 248 truck drivers were interviewed using a questionnaire and relevant information on their involvement in injury accident was collected. The result of the survey shows that 52.8% of the drivers were involved in an injury accident at least once in their professional carrier and 80% of the drivers were below the age of 40years. Results from the regression analysis found the Average distance traveled/week (p=0.0057), average driving hours/day (p=0.0232), sleeping on the wheel (p=0.0004), and presence of co-driver (p=0.0003) to be statistically significant in contributing to driver’s involvement in an injury accident. Average distance traveled/week, sleeping on wheel and presence of co-driver were found to positively affect involvement in injury accident with OR values of 1.80, 3.61 and 3.94 respectively. The model was found to be good in classifying truck drivers’ involvement in injury accident with a reasonable level of accuracy (70%). Increased safety awareness among drivers by conducting seminars and organizing training sessions regularly will help reduce the involvement of truck drivers in accidents.

Rural areas in Turkey are undergoing significant transformations, but not as much as cities. Rural areas are evolving in many aspects, including land use, economic structure, and lifestyles. Rural housing is one of the most important indicators of change. The migration of rural dwellings from settlement centers causes the settlement pattern to loosen and disperse, similar to urban sprawl. Thus, existing agricultural/livestock lands and natural elements are changing and transforming. The change/transformation process that rural areas undergo as a whole is conceptualized as “new rurality” in the literature. The adoption of the “dual lifestyle” and the “urban lifestyle” in the countryside, which are indicators of the new rurality, has increased the demand for “country residence” and “second homes” in the countryside. Thus, more housing is being built in areas near major transportation axes, particularly near cities and in rural areas. Making suitability analyses and creating projections by employing geographical data in the selection of the locations of these newly built houses is important for the future of existing agricultural areas and rural planning. Performing site selection analyses with different methods demonstrates the advantages of one method over the others and provides more accurate analyses. In this study, logistic regression (LR), analytical hierarchy process (AHP), and frequency ratio (FO) methods were used for site selection suitability analysis. The study area, Keban Stream Basin, is located in the northwest of Elazig, within the borders of the Keban district. The basin has a surface area of approximately 187 km2 . According to the results on the maps, the basin has an average land area of 12.5 km², which is suitable for new rural housing construction. Although each model has its advantages, the LR and FO methods produced more suitable results than the AHP method. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the socio-economic structure of grain maize enterprises which utilized and did not utilize from drip irrigation subsidies in Kahramanmaraş province and the factors affecting utilizing from drip irrigation support were determined. The material of the study consisted of the surveys conducted with grain maize producers who utilized and did not utilize from the drip irrigation supports provided by the Ministry of Agriculture and Forestry between 2012 and 2017. Surveys were conducted with 45 grain maize producers who used drip irrigation systems by the way of subsidies and the same number of grain maize producers who did not. Total of 45 producers were selected through simple random sampling method. The tendency of grain maize producers utilizing from drip irrigation subsidies and the factors affecting these tendencies were determined by logistic regression analysis. It was observed that the age of the producers, their education period, the number of family members, the total size of the land they cultivated and their total agricultural income positively affected the status of utilizing from drip irrigation support. On the contrary, agricultural experience, the number of people working in agriculture in their family and the production area of grain maize negatively affected. In accordance with the results, various strategies can be developed by the decision makers on the generalization of the use of drip irrigation system in the agricultural production and according to the effects of the subsidies, the efficient use of the restricted sources can be provided by developing policies. [ABSTRACT FROM AUTHOR]

COVID-19 is a pandemic in which changes and transformations are experienced in many ways from consumption patterns to daily practices at the individual and social level, and this change and transformation has different psychological reflections, especially on individuals. On the other hand, the fact that daily life has changed drastically with the measures that must be followed and the need for the cooperation of individuals on this issue has a compelling effect. In this study, logistic regression analysis was carried out with a design in which preventive behaviour defined as a categorical dependent variable, and psychological reactance, risk perception, fear and anger as independent variables. The findings obtained from 463 data collected through an online survey indicated that psychological reactance based on prohibitions and social regulations, risk perception and fear are effective in exhibiting a high level of compliance with the measures. In reference to these findings, suggestions related to message language are presented to develop the desired behavioural change in the public favoring higher compliance with the preventive behaviours. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)