Reprint of "Decision support models for supplier development: Systematic literature review and research agenda"

The continuing trend towards sourcing components and semi-finished goods for less vertically integrated manufacturing systems globally leads to a dramatic increase in supply options for companies. To ensure that companies benefit from the potentials global sourcing offers, supplier-buyer relationships need to be managed efficiently. Due to the decreasing share of value-adding activities provided in-house, suppliers are more and more considered as an essential contributor to the buying company's competitive position. Consequently, to realize and sustain competitive advantages, companies try to establish institutionalized long-term relationships to their most important suppliers and to actively improve the productivity and performance of their supplier base. To support supplier development in practice, researchers have developed decision support models that provide assistance in selecting and implementing suitable supplier development activities. The aim of this paper is to provide a comprehensive and systematic overview of decision support models for supplier development and to develop a research agenda that helps to identify promising areas for future research in this area. First, typical applications for supplier development as well as potential development measures that can be adopted to improve the performance of suppliers are identified. Secondly, a systematic literature review with a focus on decision support models for supplier development is conducted. Based on the analysis of the literature, we define a research agenda that synthesizes key trends and promising research opportunities and thus highlight areas where more decision support models are needed to foster supplier development initiatives in practice

An alternative word embedding approach for knowledge representation in online consumers’ reviews

Öz: Purchasing decisions in e-commerce shopping websites are highly influenced by online reviews. Although online reviews contain fine- grained consumers’ opinions that reflect their preferences towards products; an important challenge, is that the number of online reviews can be very huge for fast and effective analysis. Hence, discovering the thematic structure of documents plays an important role in analyzing online reviews. The proposed system in this paper aims to discover the main consumer interests in online reviews on Turkish e-commerce websites. For this aim, a novel hybrid method combining Latent Dirichlet Allocation (LDA) and word2vec is proposed. Finally, we compare the performance of our work with those of several state-of-the- art baselines on 7 datasets collected from well-known Turkish e- commerce websites. The experimental results show how our proposed approach was able to provide significantly improved performance over baselines. Besides, our method enables us to discover very specific topics complying with consumer interests

In the evaluation of endometrial pathologies of clinically referred cases with abnormal uterine bleeding, comparison of findings of tvusg, d&c and operative hysterosopy with accurate hystopathologic results. medipol university faculty of medicine, thesis in obstetrics and gynecology, İstanbul, 2017

AMAÇ: Endometriyal patolojiler sıklıkla karşılaşılan jinekolojik problemlerdendir. Hastalar çoğunlukla kliniğe anormal uterin kanama ile başvurmaktadırlar. Çalışmamızda, retrospektif olarak poliklinik şartlarında pelvik muayene ve TVUSG'la, endometriyal patoloji olabileceği düşünülen ve sonrasında dilatasyon & küretaj ve operatif histeroskopi (H/S) yapılmış vakaların patoloji sonuçlarını analiz ederek TVUSG, D&C ve H/S'nin endometriyal patolojileri saptamadaki etkinliğini karşılaştırmayı amaçladık. METOD ve MATERYAL: Çalışmamız Temmuz 2012 – Haziran 2017 tarihleri arasında Medipol Üniversitesi Tıp Fakültesi Hastanesi Kadın Hastalıkları ve Doğum Kliniğinde yapılmıştır. Tüm hastalara pelvik muayene, transvajinal ultrasonografi, ofis histeroskopi ve endometrial biopsi (D&C) yapılmıştır. Retrospektif çalışmamızda, kliniğimize anormal uterin kanama şikayeti ile başvuran, yapılan muayenelerinde ve TVUSG'lerinde endometriyal patoloji düşünülerek, operatif histeroskopi ve dilatasyon küretaj uygulanmış 160 hasta çalışmaya dahil edildi. Olguların geriye dönük olarak TVUSG bulguları, operatif H/S ve D&C bulguları tarandı. Bu yöntemlerle elde edilen histopatolojik sonuçlar karşılaştırıldı. Son patoloji sonuçları referans test olarak kabul edilerek; D&C, histeroskopinin ve transvajinal ultrasonografinin sensitivite, spesifitesi, pozitif prediktif değer ve negatif prediktif değerler hesaplandı. Tüm hastalara uygulanan transvajinal ultrason, D&C ve histeroskopi sonuçlarını literatür ışığında değerlendirerek karşılaştırdık. İstatistiksel analizde SPSS 24.O ve Microsoft Excel 2010 kullanılmıştır. BULGULAR: Histopatolojik sonuçların dağılımı: 81 (%51) ile en sık saptanan patoloji endometriyal polip, 27 (%16,7) Endometriyal hiperplazi, 21 (%13) submüköz leiomyom, 2 (%1,25) endometriyum kanseri, 29 (%18.1) normal endometriyal doku şeklinde idi. Histeroskopi eşliğinde biyopsinin endometriyal patolojileri saptamada sensitivite, spesifite, pozitif prediktif ve negatif prediktif değerleri sırasıyla %100,%87,2, %78, %100 bulunmuştur. Dilatasyon küretaj için bu değerler sırasıyla %88,8, %75,1, %42,1, %97,08, TVUSG için bu değerler sırasıyla %82,7, %%83,9, %53,3, %95,6 ve histeroskopi ön tanı için %93,1, %86,2, %60, %98,2 bulunmuştur. SONUÇ: TVUSG endometriyal patolojileri değerlendirmede non-invaziv, faydalı bir tanı metodudur. Sonuç olarak anormal uterin kanamalı hastaların ayırıcı tanısı için ilk yapılması gereken non-invaziv modalite transvajinal ultrason olup, doğru tanıyı koymak için gerekli invaziv işlemlere yönlendiren de transvajinal ultrasonografidir. Endometriyal polip ve submüköz myom gibi lokal intrakaviter lezyonlarda histeroskopi eşliğinde biyopsi, dilatasyon ve küretaja üstün bulunmuştur. Buna karşın endometriyal hiperplazi ve endometriyum kanseri gibi diffüz lezyonlarda dilatasyon ve küretajın etkinliği daha yüksek bulunmuştur. Sonuç olarak endometriyal patolojilerin tespitinde hiçbir yöntem tek başına yeterli değildir.OBJECTIVE: Endometrial pathologies are common gynecological problems. Patients are usually present with abnormal uterine bleeding. The purpose of our study is mainly to compare the effectiveness of biopsy results combined hysteroscopy and D&C and TVUS detection in the diagnosis of endometrial pathologies by retrospectively analysing the results of the endometrial pathologies that are detected by TVUS, operative hysteroscopy and D&C after the suspicion of endometrial pathology in the clinical setting. METHOD and MATERIALS: Our study took place in the Department of Obstetrics and Gynecology of Medipol University Faculty of Medicine Hospital in between July 2012 and June 2017. All patients underwent pelvic examination transvaginal ultrasonography, office hysteroscopy and endometrial sampling (D&C) through suction curettage. Our retrospective study was conducted in 160 patients who were admitted to our clinic with complaint of abnormal uterine bleeding and who were supposed to have an endometrial pathology during the TVUS which was done by us routinely after the physical examination and before the D&C and H/S. The TVUS findings, the operative hysteroscopy and D&C findings were investigated for all our cases, retrospectively. The findings acquired fom these procedures are compared. The last pathological reports were used as a reference test to calculate, sensitivity, specificity, positive predictive value, negative predictive value of the D&C, hysteroscopy and transvaginal ultrasonography. The results of the transvaginal ultrasound, D&C and the hysteroscopy which were applied to all patients compared in the literature evaluating. For the statistical analysis 24.0 SPSS and Microsoft Excel 2010 were used. FINDINGS: Histopathological results were as follows: 81 patients (51%), the most frequent pathology of endometrial polyps; 27 patients (16,%), endometrial hyperplasia; 21 patients (13%), submucous myoma; 2 patients (1,2%), endometrial cancer; 29 patients (18,1%), normal endometrial tissue. Sensitivity, specificity, positive and negative predictive values of biopsy combined with hysteroscopy for the pathology detection were found as 100%, 87,2%, 78%, 100%, respectively. On the other hand, the corresponding values for dilatation curettages were found as %67.2, %34.9, %100, and %52.3, the values for TVUS were found as 82,7%, 83,9%, 53,3%, 95,6% and the values for hysteroscopic provisional diagnosis were found as 93,1%, 86,2%, 60%, 98,2% respectively. CONCLUSION: Transvaginal ultrasonography is a useful non-invasive diagnostic method in the assessment that endometrial pathology. As a result, the first to be made for the differential diagnosis of patients with abnormal uterine bleeding, transvaginal ultrasound is non-invasive modality, transvaginal ultrasonography is also required invasive procedures that lead to the correct diagnosis for. Biopsy combined with hysteroscopy was considered to be superior to D&C for the local intracavitary lesions such as endometrial polyps and submucous myoma. On the other hand, D&C was considered to be superior to hysteroscopy fort he diffuse lesions such as endometrial hyperplasia and cancer. As a result, none of the methods of detecting endometrial pathologies is alone completely enough

Enhancement of the Heuristic Optimization Based on Extended Space Forests using Classifier Ensembles

Kilimci, Zeynep Hilal/0000-0003-1497-305XExtended space forests are a matter of common knowledge for ensuring improvements on classification problems. They provide richer feature space and present better performance than the original feature space-based forests. Most of the contemporary studies employs original features as well as various combinations of them as input vectors for extended space forest approach. In this study, we seek to boost the performance of classifier ensembles by integrating them with heuristic optimization-based features. The contributions of this paper are fivefold. First, richer feature space is developed by using random combinations of input vectors and features picked out with ant colony optimization method which have high importance and not have been associated before. Second, we propose widely used classification algorithm which is utilized baseline classifier. Third, three ensemble strategies, namely bagging, random subspace, and random forests are proposed to ensure diversity. Fourth, a wide range of comparative experiments are conducted on widely used biomedicine datasets gathered from the University of California Irvine (UCI) machine learning repository to contribute to the advancement of proposed study. Finally, extended space forest approach with the proposed technique turns out remarkable experimental results compared to the original version and various extended versions of recent state-of-art studies

Skip-Gram and Transformer Model for Session-Based Recommendation

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to the lack of user information and limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model user’s past behavior sequences and graph neural network (GNN)-based models that capture potential relationships between items, miss different time intervals in the past behavior sequence and can only capture certain types of user interest patterns due to the characteristics of neural networks. Graphic models created to improve the current session reduce the model’s success due to the addition of irrelevant items. Moreover, attention mechanisms in recent approaches have been insufficient due to weak representations of users and products. In this study, we propose a model based on the combination of skip-gram and transformer (SkipGT) to solve the above-mentioned drawbacks in session-based recommendation systems. In the proposed method, skip-gram both captures chained user interest in the session thread through item-specific subreddits and learns complex interaction information between items. The proposed method captures short-term and long-term preference representations to predict the next click with the help of a transformer. The transformer in our proposed model overcomes many limitations in turn-based models and models longer contextual connections between items more effectively. In our proposed model, by giving the transformer trained item embeddings from the skip-gram model as input, the transformer has better performance because it does not learn item representations from scratch. By conducting extensive experiments with three real-world datasets, we confirm that SkipGT significantly outperforms state-of-the-art solutions with an average MRR score of 5.58%

Enhancement of the Heuristic Optimization Based Extended Space Forests with Classifier Ensembles

Extended space forests are a matter of common knowledge for ensuring improvements on classification problems. They provide richer feature space and present better performance than the original feature space-based forests. Most of the contemporary studies employs original features as well as various combinations of them as input vectors for extended space forest approach. In this study, we seek to boost the performance of classifier ensembles by integrating them with heuristic optimization-based features. The contributions of this paper are fivefold. First, richer feature space is developed by using random combinations of input vectors and features picked out with ant colony optimization method which have high importance and not have been associated before. Second, we propose widely used classification algorithm which is utilized baseline classifier. Third, three ensemble strategies, namely bagging, random subspace, and random forests are proposed to ensure diversity. Fourth, a wide range of comparative experiments are conducted on widely used biomedicine datasets gathered from the University of California Irvine (UCI) machine learning repository to contribute to the advancement of proposed study. Finally, extended space forest approach with the proposed technique turns out remarkable experimental results compared to the original version and various extended versions of recent state-of-art studies</jats:p

A Novel Framework Leveraging Social Media Insights to Address the Cold-Start Problem in Recommendation Systems

In today&rsquo;s world, with rapidly developing technology, it has become possible to perform many transactions over the internet. Consequently, providing better service to online customers in every field has become a crucial task. These advancements have driven companies and sellers to recommend tailored products to their customers. Recommendation systems have emerged as a field of study to ensure that relevant and suitable products can be presented to users. One of the major challenges in recommendation systems is the cold-start problem, which arises when there is insufficient information about a newly introduced user or product. To address this issue, we propose a novel framework that leverages implicit behavioral insights from users&rsquo; X social media activity to construct personalized profiles without requiring explicit user input. In the proposed model, users&rsquo; behavioral profiles are first derived from their social media data. Then, recommendation lists are generated to address the cold-start problem by employing Boosting algorithms. The framework employs six boosting algorithms to classify user preferences for the top 20 most-rated films on Letterboxd. In this way, a solution is offered without requiring any additional external data beyond social media information. Experiments on a dataset demonstrate that CatBoost outperforms other methods, achieving an F1-score of 0.87 and MAE of 0.21. Based on experimental results, the proposed system outperforms existing methods developed to solve the cold-start problem