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Artificial intelligence applications in education: Natural language processing in detecting misconceptions
No full textThis study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design cycle was used. The dataset obtained from 402 teacher candidates was analysed by Natural Language Processing (NLP) methods. Data was classified using Machine Learning (ML), one of the AI tools, and supervised learning algorithms. It was concluded that 175 teacher candidates did not have sufficient knowledge about the concept of greenhouse effect. It was found that the AI algorithm with the highest accuracy rate and used to predict teacher candidates' misconceptions was Multilayer Perceptron (MLP). Furthermore, through the Enhanced Ensemble Model Architecture developed by researchers, the combination of ML algorithms has achieved the highest accuracy rate. The kappa (kappa) value was examined in determining the significant difference between the AI algorithm and the human expert evaluation, and it was found that there was a significant difference, and the strength of agreement was significant according to the research findings. The findings of the current study represent a significant alternative to the prevailing pedagogical approach, which has increasingly come to rely on information technologies in the process of improving conceptual understanding through the detection of conceptual misconceptions. In addition, recommendations were made for future studies.Ankara UniversityNo Statement Availabl
A spherical fuzzy-based DIBR II-AROMAN model for sustainability performance benchmarking of wind energy power plants
No full textWind energy power plants (WEPPs) are renewable energy generation facilities that are increasingly used today. The performance evaluations of these energy plants are generally based on technical performance. Although there are studies in the literature focusing on determining the technical performance of WEPPs, there is a lack of research on evaluating their sustainable performance. The primary motivation of this study is to develop a decision model for identifying the sustainable performance of WEPPs. The model incorporates fuzzy logic and multi-criteria decision-making (MCDM) approaches, incorporating expert opinions and both qualitative and quantitative criteria. To determine the influence levels of experts, spherical fuzzy (SF) sets are utilized. The weighting of criteria is achieved using the SF-defining interrelationships between ranked criteria II (DIBR II) method, which is a novel extension of the DIBR II method based on SF sets. Additionally, an SF-alternative ranking order method accounting for two-step normalization (AROMAN) method is developed for ranking the sustainable performance of WEPPs, building upon the AROMAN method, and adapting it to SF sets. This hybrid model is named the SF-DIBR II-AROMAN hybrid model. The step-by-step procedures of this model are presented in the research, and an algorithm for these procedures is developed. To demonstrate its practicality, a case study is conducted, focusing on WEPPs with a capacity ranging from 25 to 150 megawatts in & Ccedil;anakkale, Turkey. According to the research results, among the fifteen WEPPs in the & Ccedil;anakkale region, Saros WEPP is identified as having the best one. The robustness of the obtained results is ensured through sensitivity analyses. Based on the results of two sensitivity analysis scenarios, Saros WEPP is found to have the highest sustainable performance. The SF-DIBR II-AROMAN hybrid model results are compared with different alternative ranking method results, highlighting its superior aspects. Overall, SF-DIBR II-AROMAN is consistent and robust. The research also provides insights and managerial implications, as well as explains the benefits of the proposed model for evaluating WEPPs' sustainable performance
Investigation of the Effects of Formaldehyde on Agaricus bisporus
No full textBu çalışmanın amacı, Formaldehit’in Agaricus bisporus’ daki etkilerinin incelenmesidir. Bu amaçla misel gelişimi, vitamin değerleri, mineral değerleri ve toksik madde birikimi gibi parametreler kullanılmıştır. 1 kontrol grubu ve 3 uygulama olarak 4 farklı gruba ayrılmıştır. Laboratuvarımızda ürettiğimiz Agaricus bisporus’dan alınan spor örnekleri kullanılmıştır. Kontrol grubunda Agaricus bisporus Sabouraud Dextrose Agar besiyerinde çimlendirilmiş, uygulama gruplarında ise sırasıyla Sabouraud Dextrose Agar %2.5, %5 ve %10 Formaldehit uygulaması ile 10 gün boyunca çimlendirilmiştir. Sonuçlar kontrol grubu ile karşılaştırıldığında misel gelişiminin kontrol grubuna oranla verilen dozlara paralel olarak azaldığı, vitamin değerlerinin azaldığı ve toksik madde birikiminin arttığı tespit edilmiştir. Bu çalışmada elde edilen veriler Agaricus bisporus üretiminde Formaldehit kullanımının sınırlandırılması gerektiğini açıkça ortaya konulmuşturThe aim of this study is to examine the effects of Formaldehyde on Agaricus bisporus. For this purpose, parameters such as mycelial growth, vitamin values, mineral values and toxic substance accumulation were used. It was divided into 4 different groups as 1 control group and 3 applications. Spore samples taken from Agaricus bisporus, which we produced in our laboratory, were used. In the control group, Agaricus bisporus was germinated in Sabouraud Dextrose Agar medium, and in the application groups, it was germinated for 10 days with the application of Sabouraud Dextrose Agar 2.5%, 5% and 10% Formaldehyde, respectively. When the results were compared with the control group, it was determined that mycelial growth decreased in parallel with the doses given compared to the control group, the vitamin values decreased and the accumulation of toxic substances increased. The data obtained in this study clearly demonstrated that the use of Formaldehyde in the production of Agaricus bisporus should be limite
Information Professionals' Metaphorical Perceptions of Artificial Intelligence Concept
No full textThis study examines the metaphors used by 66 information professionals in T & uuml;rkiye to conceptualize artificial intelligence (AI). Through qualitative metaphor analysis, rich insights emerged on perceptions of AI's nature, functioning, relationships with humans, roles, and societal impacts. Some of the metaphors used are child, human, artist, human intelligence, robot, assistant, doctor, weapon, terminator, something scary, closed box, black hole, Pandora's box, etc. Multiple, often contradictory metaphors like child and uncontrollable power indicate AI is viewed in a multidimensional way, evoking both promise and concern. Key findings suggest information professionals appreciate AI's transformative potential in enhancing services but also harbor anxieties related to human relevance, technocracy, and social issues like privacy. Understanding these complex cognitive and emotional orientations can facilitate responsible AI adoption to augment information services. Gesturing at AI's multifaceted connotations also foregrounds communication challenges complicating public debate and policymaking on emerging technologies. The value of this study lies in surfacing the nuanced perspectives of a stakeholder group positioned to mediate public understanding and direct integration of AI in information services. Mapping information professionals' rich construals of AI also highlights the need for public communication attuned to metaphorical reasoning around new technologies.We would like to thank the knowledge professionals for their valuable contributions to the study
Skin lesion classification by weighted ensemble deep learning
No full textSkin cancer represents a significant global health threat with potentially fatal consequences if left undiagnosed. Early detection is crucial for successful patient treatment, yet accurate identification of skin lesions poses a challenge even for experienced dermatologists. In this context, the development of computer-aided skin lesion classification systems emerges as a promising path to empower dermatologists with the potential for earlier diagnoses and more effective treatment interventions. This study proposes a two-stage approach for early detection of skin cancer. Firstly, eight pre-trained deep architectures were tested on the ISIC dataset using transfer learning and fine-tuning. Secondly, three successful models with the highest accuracy were chosen, and ensemble learning was employed to obtain a final result. The ensemble learning method outperformed individual models, achieving a remarkable ROC AUC rate of 99.96%. DenseNet121 exhibited the highest performance among the individual models, with accuracy rates of 99.75%, 98.2%, and 99.6% for the train, validation, and test datasets, respectively. The promising results hold significant potential for early detection and treatment of skin cancer, a prevalent global disease. These findings could prove invaluable for clinics, offering valuable support to their decision-making processes and enhancing their ability to combat this widespread health concern. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024
A model proposal and solution approach for the integrated problem of fleet assi·gnment, aircraft routing and flight scheduling
No full textFen Bilimleri Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı, Endüstri Mühendisliği Bilim DalıYolcu taşımacılığında güvenlik ve hız açısından havayolu taşımacılığı daha fazla tercih edilmektedir. Talep arttıkça şirketler arasında rekabet de artmaktadır. Bu tez çalışmasında amaç havayolu operasyonel planlama problemlerini birlikte optimize ederek şirketlerin hizmet kalitesini ve müşteri memnuniyetini artırmaktır. Bunun için uçuş çizelgeleme, filo atama ve uçak rotalama problemleri birlikte dikkate alınmıştır. Havaalanlarındaki yoğunluklardan dolayı değişkenlik gösteren talep ve seyir dışı süreler stokastik olarak değerlendirilmiştir. Literatürden farklı olarak kod paylaşımı anlaşmaları, fazla rezervasyon ve istasyon saflığı özellikleri birlikte dikkate alınmıştır. Bu özellikler ile modelin gerçek hayat problemleriyle uyumu artarken, şirketlerin karlılığı ve kapasite kullanımı da optimize edilmektedir. Amaçlar doğrultusunda oluşturulan yeni problemin modellenmesi için üç aşamalı stokastik doğrusal olmayan programlama kullanılmıştır. Gerçek hayat problemleri dikkate alındığında problem boyutları büyür ve karmaşıklık artar. Mevcut yazılımlarla modelin çözümü zorlaştığı için meta-sezgisellere başvurulmuştur. Literatürdeki performanslarından dolayı tavlama benzetimi ve guguk kuşu arama meta-sezgiselleri kullanılmıştır. Stokastik değişkenlerin değerlendirilmesinde ise Monte Carlo simülasyonu kullanılarak çok sayıda senaryo değerlendirilmiştir. Ayrıca stokastik-optimizasyon yöntemlerinden örnek ortalamalar yaklaşımı meta-sezgiseller ile kullanılarak test problemlerinin çözümü sağlanmıştır. Böylece dört farklı sim-sezgisel yöntem kullanılarak çözümler ve çözüm yöntemlerinin performansları karşılaştırılmıştır. Sonuç olarak havayolu operasyonlarından karmaşıklığı yüksek, önemli bir gerçek hayat problemine sim-sezgisellerin hızından yararlanılarak kaliteli çözümler üretilmiştir. Böylece havayolu şirketlerine planlama optimizasyonu konusunda yeni ufuklar açılmıştır.Air transportation is preferred in terms of safety and speed in passenger transportation. As demand increases, competition between companies increases. This thesis aims to increase the service quality and customer satisfaction of companies by optimizing airline operational planning problems together. For this, flight scheduling, fleet assignment, and aircraft routing problems were considered together. Demand and non-cruising times, which vary due to airport congestion, were evaluated as stochastic. In this thesis, codesharing agreements, overbooking, and station purity features are considered together, unlike the literature. With these features, the model's compatibility with real-life problems increases, while companies' profitability and capacity utilization are also optimized. Three-stage stochastic nonlinear programming was used to model the new problem created in line with the objectives. When real-life problems are considered, problem sizes grow and complexity increases. Metaheuristics were used since it became difficult to solve the model with existing software. Simulated annealing and cuckoo search metaheuristics have been used due to their performance in the literature. Many scenarios were evaluated using Monte Carlo simulation for stochastic variables. In addition, the sample averages approach, one of the stochastic optimization methods, was used with metaheuristics to solve the test problems. Thus, the performances of the solutions and solution methods were compared using four different heuristic methods. As a result, quality solutions have been produced by utilizing the speed of symbolic heuristics to solve a significant real-life problem with high complexity in airline operations. Thus, new horizons have been opened for airline companies in planning optimization
Investigation of the Effects of Allicin on the Nasal Mucosa
No full textObjectives: We investigated the effects of allicin on nasal mucosa via an experimental study. Methods: In the study, 16 male New Zealand Albino (2.5-4.5 kg) rabbits were used. The right nasal passages of the 8 rabbits were included in the control group (Group 1, n = 8), and the right nasal passages of the 8 different rabbits were included in the study group (Group 2, n = 8). In the study group (Group 2), a Merocel tampon soaked in Allicin (Alli Tech; Dulwich Health) (0.5 mg/kg). In the control group (Group 1), a Merocel tampon soaked in serum physiologic was placed in the right nasal passage for 3 days (first to third days of the study). On the fourth day, nasal mucosa was excised and histopathological examinations were performed. Results: Our results showed that there were no significant differences in terms of bleeding, congestion, inflammation, calcification, and seromucous gland density between the study and control groups (P > .05). In light microscopic evaluation, moderate density of lymphocytic cells beneath the surface epithelium and, further down, seromucous gland structures, dense seromucous glands, and occasional ductal structures were observed in the study group. Congested vascular structures beneath the respiratory epithelium and adjacent to a thick-walled vascular structure, coarse calcification is observed in the control group. Conclusion: It has been demonstrated that the Allicin-soaked Merocel pack does not have adverse effects on rabbit nasal mucosa, and it does not lead to mucosal bleeding, congestion, inflammation, and calcification, and changes in the seromucous gland density. Considering the antibacterial and antiviral effects of allicin, it is appropriate to plan research in humans to evaluate its use in nasal packs applied during epistaxis, septoplasty, rhinoplasty, and endoscopic sinus surgery
Accuracy of Large Language Models in ACR Manual on Contrast Media-Related Questions
No full text[Abstract No tAvailable]Funding The study was not supported by any funding
Developing a hybrid methodology for green-based supplier selection: Application in the automotive industry
No full textThe green performance values of businesses are of great importance in terms of sustainability, which includes long-term economic, social, and environmental effects. Thus, today, enterprises and managers are increasingly interested in this issue, and related topics, including supplier selection, have been inserted into decision-making procedures. However, how to predict the effects of green performance criteria, which represent environmental sustainability, on social and economic sustainability remains unclear. In this regard, the main purpose of this research is to develop a supplier selection methodology considering green performance criteria by applying multiple regression analysis and the Evidential Fuzzy Multi-Criteria Decision Making (F-MCDM) method based on Dempster-Shafer Theory (DST), which are both powerful methods in statistical analysis and decision-making under uncertainty. In the first phase of the research, variables that significantly affect green performance have been determined by testing the eight generated hypotheses with multiple regression analysis. Then, the best supplier was determined using those green supplier selection criteria in the Evidential F-MCDM method. Since using environmentally hazardous paints in the production process continues, the automobile paint production sector has been chosen as the application area of this green-based supplier selection methodology. In this respect, green dynamic capacity, green purchasing, eco-design, investment recovery, and green product innovation variables have been inserted into the Evidential F-MCDM method as the determinant variables of green performance. This research reveals that integrating multiple regression and Evidential F-MCDM methods can be a hybrid methodology in supplier selection. Thus, a different perspective is introduced into the green supplier selection decision-making process by considering the effects of criteria in the MCDM model on green performance. This innovation enhances the criteria determination and selection processes in classical MCDM approaches. In addition, green dynamic capacity is the most critical criterion in supplier selection based on their green performance, especially in the scope of this research
Evaluation of Social Appearance Anxiety, Self-Esteem, Eating Behavior, and Body Image in Rhinoplasty and Septoplasty Patients
No full textThis study aimed to compare the relationship between social appearance anxiety, self-esteem, eating behavior, and body perception in individuals who applied to the Ear, Nose and Throat outpatient clinic for rhinoplasty and septoplasty. A total of 93 people were included, 44 patients in the rhinoplasty group and 49 patients in the septoplasty group. Social Appearance Anxiety Scale, Rosenberg Self-Esteem Scale, Dutch Eating Behavior Questionnaire (DEBQ), and Stunkard Scale (Body Image Scale) were applied to the individuals. The researchers took measurements of the participants' body weight (kg) and height (cm). The data obtained were analyzed using the statistical package program (SPSS). Rhinoplasty patients were found to have higher social appearance anxiety and lower self-esteem compared to septoplasty patients (p0.05). In both groups, a negative correlation was found between self-esteem and social appearance anxiety. However, this relationship was stronger in rhinoplasty patients (r=-0.579) compared to septoplasty patients (r=-0.331) (p<0.05). In both groups, restrictive eating and negative body image were positively correlated with BMI (p<0.05). A significant relationship was also found between BMI and emotional eating in the septoplasty group (r=0.474, p<0.05). Our study has shown that females who want to have rhinoplasty have higher social appearance anxiety and lower self-esteem. These findings demonstrated that comprehensive psychological assessment is important to improve both the mental health and overall outcomes of patients undergoing nasal surgery