Sivas Cumhuriyet University Research Information System
Not a member yet
61356 research outputs found
Sort by
Evaluation of Parents’ Information Sources Perceptions and Attitudes Regarding Fluoride Toothpastes and Topical Fluoride Applications: A Cross-Sectional Study
No full textOSMANLI SINIR ARAŞTIRMALARINDA HUDÛD CERİDELERİ VE MEVKİ İSTİHDAM CETVELLERİNİN ÖNEMİ: 1919 OSMANLI BULGAR HUDÛDU ÖRNEĞİ
No full textNadir Bir Telomeropatinin Klinik Yansıması: Miyelodisplastik Sendrom ile Seyreden Diskeratozis Konjenita
No full text
HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysis
No full textHematological biomarkers have emerged as powerful tools in diagnosing Acute Heart Failure (AHF). This study introduces a novel diagnostic framework that integrates Explainable Artificial Intelligence (XAI) with Morris Sensitivity Analysis (MSA) to enhance both the interpretability and performance of machine learning models in AHF detection. A dataset consisting of 425 AHF patients and 430 controls was analyzed using eight machine learning models, including XGBoost, Histogram-based Gradient Boosting (histGB), Explainable Boosting Machine (EBM), and Random Forest. Model performance was evaluated through metrics such as AUC, accuracy, precision, recall, and Brier score. Hyperparameters were optimized via Bayesian optimization. Feature importance was assessed using MSA to identify variables with the highest predictive influence. The histGB model achieved the highest performance with an AUC of 87.93%. Both MSA and EBM consistently identified PDW, RDW-CV, NEU, NEU/LY ratio, age, and WBC as top predictive features across multiple models. These hematological markers demonstrated strong potential for early diagnosis and risk stratification in AHF patients. This study presents a clinically relevant, interpretable, and cost-effective diagnostic strategy that combines XAI with MSA for AHF prediction. The framework enhances clinical trust and provides a pathway toward personalized treatment by identifying accessible hematological biomarkers. The integration of explainability into AI models improves their transparency and applicability in real-world clinical settings
Modern Satış Yönetimi: Teori, Strateji ve Uygulama Perspektifi
No full textTeknoloji çağının erken dönemi veya yapay zekâ çağı olarak adlandırabileceğimiz 21. Yy.’ın ilk çeyreği tamamlanırken yeni pazarlama iletişim dili “Dijitalleşme” olmuştur. Dijitalleşme çağının günümüzdeki önemini anlamak için pazarlamanın değişim sürecini de anlamak gerekmektedir. Pazarlamanın temelini oluşturan tüketici ihtiyaç ve tatmini dönemsel olgu ve gerekçelerle değişime uğramıştır. Ürün merkezli birinci sanayi devrimi, yerini tüketici merkezli pazarlama anlayışına bırakmıştır. İnternetin ve dijitalleşmenin hayatımızda daha çok yer aldığı dönemde ise internet tabanlı pazarlama ve dijital pazarlama kavramlarının yükselişi dijital işletme çağını başlatmıştır. İnternetin her ev ve iş yerinde ulaşılabilir hale gelmesi ticari işlemleri kolaylaştırıp hızlandırırken pazarlama bilimi için pazar kavramını da kökten değişikliğe uğratmıştır. İşletmeler artık sadece mağaza veya şube açtığı yeri değil tüm evreni pazarı olarak görmektedir. Dolayısıyla dijital pazarlamayla pazar, artık her yerdir. Pazar yerindeki bu değişim günümüz dijital anlayışına uygun olarak işletmelerin ürettiği ürünleri satarken kullandığı kanalları da farklılaştırmıştır. Dijitalleşmeyi en temel satış elemanı olarak gören işletmeler birçok uygulama kullanarak iş sistemleri entegrasyonunu optimize etmektedirler. Bu uygulamaların başında CRM sistemleri, yapay zekâ ile veri analitiği, nesnelerin interneti (IoT), artırılmış gerçeklik (AR), sanal gerçeklik (VR), otomasyon araçları, omnichannel ve multichannel satış yöntemleri sohbet robotları (chatbotlar), sanal asistanlar, bulut tabanlı çözümler ve mobil uygulamalar kullanarak iş sistemleri entegrasyonunu optimize etmektedirler.As the early period of the technological age, or what we might call the age of artificial intelligence, comes to a close in the first quarter of the 21st century, the new language of marketing communication has become “digitalization.” To understand the importance of the digitalization era today, it is also necessary to understand the process of change in marketing. The consumer needs and satisfaction that form the basis of marketing have undergone change due to periodic phenomena and reasons. The product-centric first industrial revolution has given way to a consumer-centric marketing approach. In an era where the internet and digitalization have become more prevalent in our lives, the rise of internet-based marketing and digital marketing concepts has ushered in the age of digital business. The accessibility of the internet in every home and workplace has facilitated and accelerated commercial transactions while also fundamentally changing the concept of the market for marketing science. Businesses now view the entire universe as their market, not just the location where they open a store or branch. Therefore, with digital marketing, the market is now everywhere. This change in the market has also diversified the channels businesses use to sell their products in line with today's digital understanding. Businesses that view digitalization as the most fundamental sales element are optimizing their business system integration by using various applications. Among these applications are CRM systems, artificial intelligence and data analytics, the Internet of Things (IoT), augmented reality (AR), virtual reality (VR), automation tools, omnichannel and multichannel sales methods, chatbots, virtual assistants, cloud-based solutions, and mobile applications.</p
Impact of de novo metastatic breast cancer on survival of patients: A comparative retrospective observational study
No full textAim: The aim is to compare the characteristics of de nova metastatic BC (dnMBC) and recurrent metastatic BC (rMBC). Materials and Methods: The study included female patients diagnosed with histologically dnMBC and rMBC who received treatment at a tertiary care center from 2010 to 2019. Medical records were utilized to collect information regarding the patients’ tumors, alongside clinical and demographic characteristics. Each patient’s overall survival (OS) was determined starting from the moment they were diagnosed with MBC. The patients with dnMBC and rMBC were compared statistically based on their clinical and sociodemographic features. Results: Out of the 322 patients, 213 (66.1%) had rMBC, and 109 (33.9%) had dnMBC. Patients with dnMBC were older (p0.05) differed significantly between the groups. Conclusion: There was no difference in OS. Clinic subtype, tumor grade, and treatment modalities may confuse the survival outcomes in BC patients
Quadratic Box-Behnken-designed microextraction approach using organic acid-assisted magnetic deep eutectic solvent for the spectrophotometric determination of carmoisine (E122) in foodstuffs, soft drinks, and cosmetic products
No full textA simple, rapid, hand-shaking-assisted, and practical Box-Behnken-designed microextraction approach using a newly synthesized Organic Acid-assisted Magnetic Deep Eutectic Solvent (OA-MDES) was established for the first time for the UV–vis spectrophotometric determination of Carmoisine (E122) in foodstuffs and cosmetics. Five different types of OA-MDES were prepared to achieve the quantitative microextraction of Carmoisine. The important independent microextraction variables, including pH, amount of NaCl, and the type and volume of the dispersive solvent and OA-MDES, were investigated and optimized using chemometric Box-Behnken experimental design. The quadratic microextraction model with R2 = 0.9997 was designed as the best-fitting chemometric approach. Limit of detection, preconcentration factor, and linear dynamic range were determined as 0.03 μg/mL, 100, and 0.1–150 μg/mL, respectively. The determination of Carmoisine in real samples was conducted using analyte addition-recovery tests. Carmoisine contents of foodstuffs, soft drinks, and cosmetic samples were determined to be between 7.3 and 22.1 μg/mL and 16.7–166.0 μg/g, with microextraction recovery values ranging from 89 % to 98 %. Analytical GREEnness and Blue Applicability Grade Index metric tools were used to evaluate the sustainability of the method. Analytical greenness score of 0.70/1.00 and applicability point of 67.5/100 were determined for the developed OA-MDES microextraction method
A Novel Approach to Model Ensembled-Based ANFIS for Big Data
No full textThis study aims to develop a new ANFIS-based ensemble modeling approach that provides high prediction accuracy and generalization capability on large datasets. The proposed approach utilizes the parallel processing capacity of the MapReduce algorithm to divide large datasets into smaller chunks and create and train independent ANFIS models for each chunk. While the input and output membership functions obtained from the trained structures are directly transferred to the new architecture, the rule bases are integrated using the rule adjustment function. The number of rules has been significantly reduced compared to the classical ANFIS structure. In this way, both the computational cost has been reduced and the model complexity has been effectively managed. In traditional ensemble approaches found in the literature, the output values of the models are generally combined, whereas in this study, the proposed approach combines the ANFIS structures obtained from each subset of the data to create a single ANFIS-based ensemble model. The obtained results demonstrate that a single ensemble system architecture, encompassing the entire large dataset and possessing high generalization capability, has been successfully created