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Facial Emotion Recognition Using Residual Neural Networks
No full textSerap Kırbız / 0000-0001-7718-3683Facial emotion recognition (FER) has been an emerging research topic in recent years. Recent automatic FER systems generally apply deep learning methods and focus on two important issues: lack of sufficient labeled training data and variations in images such as illumination, pose, or expression-related variations among different cultures. Although Convolutional Neural Networks (CNNs) are widely used in automatic FER, they cannot be used when the number of layers is large. Therefore, a residual technique is applied to CNNs and this architecture is named residual neural network. In this paper, an automatic facial emotion recognition method using residual networks with random data augmentation is proposed on a merged FER dataset consisting of 41,598 facial images of size 48 x 48 pixels from seven basic emotion classes. Experimental results show that ResNet34 with data augmentation performs better than CNN with a classification accuracy of 81%
Disentangling the Dynamic Digital Capability, Digital Transformation, and Organizational Performance Relationships in Smes: a Configurational Analysis Based on Fsqca
No full textWhile digitalization has become inevitable for firms of every size, a limited number of studies to date aimed to investigate the impact of digital capabilities and digital transformation on the organizational performance of small businesses. Drawing on the dynamic capabilities view, the current study analyzes the conditions under which the dynamic digital capability of a small and medium-sized enterprise (SME) would lead to higher performance. In this study, a unique fuzzy-set qualitative comparative analysis methodology was used for analyzing the data collected from 136 SMEs for investigating the IT utilization, human capital, digital maturity, and digitalization strategy antecedents of dynamic digital capability. The results reveal that two particular configurations of dynamic digital capability are identified as the main digitalization influencers of organizational performance in SMEs. To the best of our knowledge, this study presents the first empirical findings to the literature about dynamic digital capability and organizational performance relationships in SMEs through the utilization of configurational analysis methodology. Theoretically, the study addresses an acknowledged need for a holistic approach to uncover the underlying mechanisms of dynamic digital capability formation and digital transformation in small firms, with their impact on firm performance. The findings also present vital practical implications for business owners, policy-makers, and bodies responsible for SMEs, by providing new insights about the combination of factors that drive high performance, particularly at times of turbulence, in these units
The Mediating Role of Cultural Intelligence in The Relationship Between Personality and Cross Cultural Adaptability: A Research on Graduate Students
No full textKüreselleşme hızlandıkça ve ülkelerin çevresel koşulları değiştikçe, bireylerinyaşam standartlarını arttırmak amacıyla, farklı ülkelerde yaşama isteği artmaktadır.Bu istek bireylerde her ne kadar güçlü olsa da, farklı bir kültüre adaptasyon sağlamakhiç de kolay olmamaktadır. Bireylerin kültürlerarası adaptasyonlarını sağlamasınaetki eden çeşitli faktör bulunmaktadır. Bu faktörlerin başında bireylerin farklıkültürden bireyleri anlamasını ve o kültüre uygun davranmasında önemli bir roloynayan kültürel zeka kavramı gelmektedir. Kültürel zeka analizi, bireylerin buzorlukların üstesinden gelmesine veya bu zorluklardan kaçınmasına yardımcı olmakiçin önemli bir konumda olmaktadır. Kültürel zekanın yanı sıra bireylerin kişilikleride sonuçlara önemli derecede etki eden bir diğer unsur olarak ele alınmaktadır.Bireylerin kişilik yapılarının analizi, bu bireylerin farklı kültürlere adaptasyonsağlayıp sağlayamayacakları hakkında ön görüler ortaya koyabilmektedir.Individuals' desire to live in different nations to improve their living standards grows as globalization accelerates and countries' environmental conditions change. Even though people have a strong desire to do so, it is extremely difficult to adapt to a new culture. There are numerous factors that influence people's cross-cultural adaptability. The concept of cultural intelligence, which plays an important role in helping individuals understand individuals from different cultures and behave appropriately in that culture, is the most important of these factors. Cultural intelligence analysis can help individuals overcome or avoid these challenges. Individuals' personalities, in addition to cultural intelligence, are considered as another factor that has a significant impact on the results. The analysis of personality structures can reveal whether or not they can adapt to different cultures. The purpose of this study is to investigate the concepts of cross-cultural adaptability, cultural intelligence, and personality in the context of Business Administration graduate students' intentions to live abroad and to determine the extent to which these three concepts are related
Bir Siyasi Davanın Anatomisi: Barış için Akademisyenler Vakası Egemenlik Gösterisi Olarak Dava ve Hakikatin Tersi Yüzü
No full text2016’nın Ocak ayında Türkiye’nin ve dünyanın çeşitli üniversitelerinden 1128 akademisyen ve araştırmacı, kısaca “Barış Bildirisi” olarak bilinen “Bu suça ortak olmayacağız” başlıklı metinle, hükümetin Güneydoğu illerinde ilan ettiği süresiz sokağa çıkma yasaklarını ve yasaklar sırasındaki askeri operasyonlarda kullanılan orantısız kolluk gücünü protesto ettiler. Akademisyenler ayrıca hükümete, 2015’te kesintiye uğrayan barış görüşmelerine geri dönme çağrısı yaptılar. Hükümetin güvenlik politikalarını kınayan ve temel insan haklarını koruma çağrısı yapan, ülke tarihinde daha önce görülmemiş kitlesellikteki bu akademisyen girişimi, bildiri yayınlandığı andan itibaren hemen her resmî merciinin seferber edildiği, örgütlü ve çok yönlü bir siyasi baskıyla karşı karşıya kaldı. Bu siyasi baskının bir ayağı da, bildiri imzacılarının terör örgütü propagandası yapma suçlamasıyla yargılandıkları ve mahkûm edildikleri ceza davalarıydı. Bu çalışma, Barış Bildirisi imzacılarının yargılandığı üç yıllık sürece odaklanarak, ceza davasının siyasi rejimin düşman olarak işaretlediği grupları bastırmak için hangi yöntemlerle inşa edildiğini ve otoriter bir rejimde baskı aygıtı olarak yargıya nasıl işlev yüklendiğini incelemeyi amaçlıyor. Çalışma aynı zamanda, Barış için Akademisyenler vakası üzerinden, itham edilen politik öznelerin siyasi davayı bir itiraz ve direniş alanına dönüştürme ve failliğin anlamını ters yüz etme imkânını tartışmaya açıyor
Elektronik Kaynaklar Hizmetinde Yapay Zeka Destekli Yenilikçi Yaklaşımların İncelenmesi
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Mechanochemical Synthesis and Characterization of Nanostructured Erb4 and Ndb4 Rare-Earth Tetraborides
No full textRare-earth borides have become very popular in recent decades with high mechanical strength, melting point, good corrosion, wear, and magnetic behavior. However, the production of these borides is very challenging and unique. The production of ErB4 and NdB4 nanopowders via mechanochemical synthesis (MCS) is reported in this study first time in the literature. Er2O3 or Nd2O3, B2O3, and Mg initial powders are mechanically alloyed for different milling times to optimize the process. Rare-earth borides with MgO phases are synthesized, then MgO is removed with HCl acid. The nanostructured rare-earth tetraboride powders are analyzed using X-ray diffraction (XRD). Based on the XRD, ErB4 powders are produced successfully at the end of the 5 h milling. However, the NdB4 phase does not occur as the stoichiometric ratio, so the B2O3 amount is decreased to nearly 35 wt%. When the amount of B2O3 is decreased to 20 wt%, NdB4 and NdB6 phases are 50:50 according to the Rietveld analysis. However, a homogenous NdB4 phase is obtained with 30 wt% loss of B2O3. The average particle sizes of ErB4 and NdB4 powders are nearly 100.4 and 85.6 nm, respectively. The rare-earth tetraborides exhibit antiferromagnetic-to-paramagnetic-like phase transitions at 18 and 8.53 K, respectively. © 2024 The Author(s). Advanced Engineering Materials published by Wiley-VCH GmbH.Istanbul Technical University Scientific Research Projects; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; National Academic Network and Information Center; Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi, BAP, (MYL202243606); Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi, BA
The Race To Sustainability: Decoding Green University Rankings Through a Comparative Analysis (2018-2022)
No full textThis study investigates the evolving landscape of green universities by analyzing and comparing rankings from 2018 to 2022. It expands beyond the single score offered by the UI GreenMetric, employing Multi-Criteria Decision-Making (MCDM) techniques to evaluate universities from diverse perspectives. Focusing on the top 50 universities from 2022, the study assesses their performance across six key criteria: setting and infrastructure, energy and climate change, waste, water, transportation, and education and research. Various MCDM methods (LOPCOW MEREC, CoCoSo, CRADIS, EDAS, MABAC, MAIRCA, and MARCOS) are implemented, revealing how they prioritize different aspects of sustainability. Furthermore, the study examines the correlation between rankings and employs the COPELAND aggregation approach to derive a unified ranking. This investigation not only contrasts MCDM outcomes with the UI GreenMetric's total score-based rankings but also illuminates the relative significance of each criterion and its variation across weighting techniques. Additionally, the study delves into the temporal dynamics of university rankings, offering insights into institutional performance across different years.DAS:The data that support the findings of this study are available from the corresponding author upon reasonable request
Gözetimli makine öğrenmesi algoritmaları kullanılarak e- ticaret satışlarının tahminlenmesi
No full textE-ticaretin gelişimi için temel olan öngörüsel analitiklere büyük ölçüde bağımlı olan büyüyen e-ticaret manzarası, operasyonel verimliliği ve stratejik karar alma süreçlerini yönlendirmektedir. Bu tez, makine öğrenimi algoritmalarının teorik temellerine derinlemesine inerken, çevrimiçi ticaretin büyümesini kolaylaştırmadaki evrimini ve kilit rolünü sergilemektedir. Bu araştırma, geniş çaplı bir e-ticaret veri kümesinin analizi yoluyla satış desenlerini öngörme odaklıdır. Öngörme, e-ticaret işletmelerinde çeşitli kritik işlevler için bir anahtar görev üstlenmektedir. Envanter yönetimini, optimal stok seviyelerini ve düzenli teslimatları sağlayarak, stratejik varlık yönetimi aracılığıyla finansal planlamayı, dinamik fiyatlandırma stratejilerini ve etkili teslimat operasyonlarıyla müşteri memnuniyetini artırmayı içeren çok yönlü uygulamaları kapsar. Ayrıca, öngörme, özelleştirilmiş kampanyaları ve akıllı bütçe tahsisini mümkün kılarak pazarlama çabalarını geliştirmede de temel bir rol oynamaktadır. Makine öğrenimi algoritmalarının entegrasyonu, bu işlevleri güçlendirmektedir. Bu araştırmanın merkezinde e-ticaret alanındaki temel bir satış tahmini görevi bulunmaktadır ve özellikle kampanya değişkenlerini entegre etme odaklıdır. Altı farklı makine öğrenimi algoritmasını kullanarak, çalışma en doğru ve açıklayıcı modeli belirlemeyi amaçlamaktadır. Dikkat çekici bir şekilde, araştırma LGBM'yi en uygun algoritma olarak belirler. Önceki tahmin çalışmalarında nadiren keşfedilen kampanya değişkenlerinin dahil edilmesi, ilginç içgörüler sunar. Ancak, başlangıçtaki varsayımların aksine, SHAP analizi, kampanya değişkenlerinin modelin açıklanabilirliği üzerinde daha az etkiye sahip olduğunu ortaya koymaktadır. Bu sınırlama farkındalığını kabul eden çalışma, değişkenleri etkili bir şekilde temsil etmek için kümeleme algoritmalarını kullanarak modelin açıklanabilirliğini artırma potansiyeline dikkat çekmektedir.The burgeoning landscape of e-commerce relies significantly on predictive analytics to drive operational efficiency and strategic decision-making. This thesis delves into the theoretical underpinnings of machine learning algorithms, showcasing their evolution and pivotal role in facilitating the growth of online commerce. At its core, this research centers on forecasting sales patterns through the analysis of an extensive e- commerce dataset. Forecasting stands as a linchpin for various critical functions within e-commerce enterprises. Its multifaceted applications encompass inventory management, ensuring optimal stock levels and streamlined deliveries, financial planning through astute asset management, dynamic pricing strategies, and the enhancement of customer satisfaction via efficient delivery operations. Furthermore, forecasting plays a pivotal role in refining marketing endeavors, enabling tailored campaigns and judicious budget allocation. The integration of machine learning algorithms fortifies these functionalities. Central to this research is the foundational task of sales prediction in the e- commerce realm, with a specific emphasis on integrating campaign variables. Leveraging six diverse machine learning algorithms, the study aims to discern the most accurate and explicable model. Remarkably, the investigation identifies LGBM as the most suitable algorithm. Notably, the inclusion of campaign variables, an aspect seldom explored in prior studies concerning forecasting, yields intriguing insights. However, contrary to initial presumptions, the SHAP analysis reveals a lesser influence of campaign variables on the model's interpretability. Acknowledging this limitation, the study highlights the potential for augmenting model interpretability by employing clustering algorithms to effectively represent variables, as outlined in the limitations section
An Overview on the Structural Monitoring, Assessment and Retrofitting of Historical Structures With a Focus on 13th Century Monuments
No full textMonumental historical structures affirm natural and cultural identity and hence they should be transmitted to future generations. The protection and preservation of these structures against aging and natural hazards, particularly seismic actions, requires a comprehensive approach including diagnosis of the present condition of the structure and enhancement of structural capacity for disaster mitigation, if necessary. It is obvious that due to their historical values, any attempt towards the preservation of the monumental historical structures must be carried out with respect to the principles of integrity and authenticity. In this study, the structural performance assessment procedures, implementation of structural health monitoring systems and seismic strengthening strategies are discussed and described with reference to 13th-century monumental historical structures in Turkiye. The structural engineering aspects of recent activities for the restoration and preservation of the Great Mosque and Hospital of Divrigi (a world heritage listed structure) and Sivas Ulu Cami (Mosque) Minaret are briefly presented. In light of the structural analysis and monitoring results, recommendations for interventions to these monumental structures are outlined.Ministry of Culture and Tourism of Turkiye; Directorate General of FoundationsThe authors would like to express their sincere gratitude to Directorate General of Foundations and Ministry of Culture and Tourism of Turkiye for their generous supports during the study. Contractors of the restoration project of theGreat Mosque and Hospital of Divrigi, Kadioglu Construction and Dor Yapi Construction companies and Contractor of the restoration project of Sivas Ulu Cami (Mosque) Minaret, Rise Engineering ; Consultancy Company are also acknowledged. Architect Nicola Berlucchi is also acknowledged for his support during the studies of both projects
The role of pearl’s causal framework in empirical research
No full textThis paper underscores the necessity of formulating precise research questions that clarify causal relationships rather than simply identifying correlations and highlights the perils of relying solely on regression analysis in tackling complex causal inquiries without causal diagrams or structural causal models. It introduces Judea Pearl's causal epistemology, including causal graphs, structural causal models, and do-calculus as vital tools for estimating causal effects. It extends to the challenges of confounding and collider effects, the application of do-calculus with basic examples from Law ; Economics and the advancements in causal discovery methods through constraint-based algorithms. The paper also offers a brief roadmap on best practices for identification and estimation