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The continuous development of technology increases the number of new inventions and thus the number of patents. Each new technology is assigned to one of the existing patent classes, otherwise, a new technology must be added to the patent classes. These processes support the development of automated patent classification methods and articles on this subject. For decades, studies have been conducted in the field of automatic patent classification. In this paper, the studies and pat ents published in this field have been evaluated using a bibliometric approach. The most referenced important studies on this subject are examined in detail, the analysis is detailed by making the pioneering authors of the field, journals, countries, assignees, etc., the focal point. The most recent developments in the field have been examined and evaluations have been presented regarding topics that are likely to be studied in the future.

Twitter is an intensely utilized platform for disaster events and emergencies. Therefore, Twitter is an important resource for providing the essential information. Named entity recognition (NER), which is the process of determining the elementary units in a text and classifying them with pre-defined categories, plays a significant role to extract essential and usefulness information. However, NER is a challenging task due to the utilized informal text in the Twitter platform such as grammatical errors and nonstandard abbreviations. In this paper, recurrent neural network (RNN)-based approaches considering diversity of activation functions and optimization functions with NER tools are utilized to extract named entities such as organization, person, and location from the tweets. Inputs for RNN models are provided via two different NER tools which are natural language toolkit (NLTK) and general architecture for text engineering (Gate). Then, pre-labeled data are trained via GloVe word embedding technique, and RNN model variants such as LSTM, BLSTM, and GRU are demonstrated. Therefore, outperforming models among RNN variants are presented for predicting named entities. Yellowbrick interpreter is used for evaluation of the proposed method and Wilcoxon signed-rank test are applied on results of two different data sets to demonstrate consistency of the proposed method. In addition, comparison is made with existing machine learning methods. The experiments by utilizing the Nepal earthquake Twitter data set show that the RNN-based approaches achieve good results in finding named entities. In emergencies, the results of this paper can help in reducing the efforts of event location detection and provide better disaster management.

Intensity of natural disasters has substantially increased; disaster management has gained importance along with this reason. In addition, social media has become an integral part of disaster management. Before, during and after disasters; people use social media and large number of output is obtained through social media activities. In this regard, Twitter is the most popular social media tool as micro blogging. Twitter has also become significant in complex disaster environment for coordinating events. It provides a swift way to collect crowd-sourced information. So, how do humanitarian organizations use Twitter platform? Humanitarian organizations utilize resources and related information while managing disasters. The effective use of social media by humanitarian agencies causes increased peoples’ awareness. The international federation of red cross and Red Crescent Societies (IFRC) is the most significant humanitarian organization that aims providing assistance to people. Thus, the aim of this paper is to analyze IFRC’s activities on Twitter and propose a perspective in the light of theoretical framework. Approximately, 5201 tweets are passed the pre-processing level, some important topics are extracted utilizing word labeling, latent dirichlet allocation (LDA model) and bag of Ngram model and sentiment analysis is applied based on machine learning classification algorithms including Naïve Bayes, support vector machine SVM), decision tree, random forest, neural network and k-nearest neighbor (kNN) classifications. According to the classification accuracies, results demonstrate the superiority of support vector machine among other classification algorithms. This study shows us how IFRC uses Twitter and which topics IFRC emphasizes more. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.

The investment budget allocated by companies in R&D activities has increased due to increased competition in the market. Applications for industrial property rights by countries, investors, companies, and universities to protect inventions obtained as an outcome of investments have also increased. The selection of the patent to be invested becomes more difficult with the increasing number of applications. Therefore, predicting patent quality is quite significant for companies to be successful in the future. The level to which a patent meets the expectations of decision makers is referred to as patent quality. Patent indices represent decision makers' expectations. In this study, an approach is proposed to predict patent quality in practice. The proposed approach uses supervised learning algorithms and analytic hierarchy process (AHP) method. The proposed approach is applied to patents related to personal digital assistant technologies. The performances of individual and ensemble machine learning methods have been also analyzed to establish the prediction model. In addition, 75% split ratio and the five-fold cross-validation methods have been used to verify the prediction model. The multilayer perceptron algorithm has 76% accuracy value. The proposed prediction model is essential in directing R&D studies to the right technology areas and transferring the incentives to patent applications with a high quality rate.

With the increase in accumulated data and usage of the Internet, social media such as Twitter has become a fundamental tool to access all kinds of information. Therefore, it can be expressed that processing, preparing data, and eliminating unnecessary information on Twitter gains its importance rapidly. In particular, it is very important to analyze the information and make it available in emergencies such as disasters. In the proposed study, an earthquake with the magnitude of Mw = 6.8 on the Richter scale that occurred on January 24, 2020, in Elazig province, Turkey, is analyzed in detail. Tweets under twelve hashtags are clustered separately by utilizing the Social Spider Optimization (SSO) algorithm with some modifications. The sum-of intra-cluster distances (SICD) is utilized to measure the performance of the proposed clustering algorithm. In addition, SICD, which works in a way of assigning a new solution to its nearest node, is used as an integer programming model to be solved with the GUROBI package program on the test data-sets. Optimal results are gathered and compared with the proposed SSO results. In the study, center tweets with optimal results are found by utilizing modified SSO. Moreover, results of the proposed SSO algorithm are compared with the K-means clustering technique which is the most popular clustering technique. The proposed SSO algorithm gives better results. Hereby, the general situation of society after an earthquake is deduced to provide moral and material supports.

Bu çalışmada, F-16 savaş uçaklarının bakım operasyonlarında kullanılmak üzere yaratıcı problem çözme teorisi (TRIZ) kullanılarak geliştirilmiş bir ekipmanın, Kalite Fonksiyonu Göçerimi (KFG) ile kullanıcı isteklerine göre yeniden tasarımına yönelik bir uygulama yapılmış ve daha iyi bir çözüm önerilmiştir. Söz konusu ekipman, Türk Silahlı Kuvvetleri (TSK) tarafından askeri havacılıktaki bakım faaliyetleri için hâlihazırda kullanılmaktadır. Ekipmanın yeni kullanılmaya başlanmış olması nedeniyle geliştirilebileceği ve kullanıcı görüşleri doğrultusunda iyileştirilebileceği değerlendirilmiştir. Bu nedenle, yazında müşteri/kullanıcı görüşleri doğrultusunda (özellikle yeni) ürün geliştirme sürçlerinde kullanılan KFG uygulaması bu çalışmada sunulmuştur. Çalışma kapsamında oluşturulan kalite ekibinden görüşme yoluyla elde edilen verilerden hareketle müşteri gereksinimleri belirlenmiştir. Daha sonra teknik gereksinimlerin de belirlenmesiyle, KFG uygulamasının önemli girdileri elde edilmiş ve kalite evinin oluşturulmasıyla, kullanılmakta olan ekipmanın kullanıcı görüşleri doğrultusunda nasıl geliştirilmesi gerektiğine ilişkin bilgiler derlenmiştir. Bu bilgilerden hareketle, ekipmanın yeniden tasarımına yönelik iyileştirme önerilerinde bulunulmuştur. Çalışmanın yazına iki önemli katkısı bulunmaktadır. Birincisi, mevcut durumda literatürde tanıtılan bir ekipman bu çalışma ile yeniden tasarlanarak alternatif yeni bir ürün önerilmiştir. İkincisi, KFG kullanılarak sistematik şekilde ürün geliştirmeye yönelik bir gerçek hayat uygulaması adım adım sunulmuştur. Çalışma kapsamında sunulan iyileştirme önerilerinin dikkate alınmasıyla, geliştirilen yeni ekipmanın üretilmesi ve kullanılmasının havacılık sektöründeki bakım operasyonlarındaki etkinliği ve verimliliği arttıracağı düşünülmektedir.

Purpose This study aims to propose a service quality evaluation model for health-care services. Design/methodology/approach In this study, a service quality evaluation model is proposed based on the service quality measurement (SERVQUAL) scale and machine learning algorithm. Primarily, items that affect the quality of service are determined based on the SERVQUAL scale. Subsequently, a service quality assessment model is generated to manage the resources that are allocated to improve the activities efficiently. Following this phase, a sample of classification model is conducted. Machine learning algorithms are used to establish the classification model. Findings The proposed evaluation model addresses the following questions: What are the potential impact levels of service quality dimensions on the quality of service practically? What should be prioritization among the service quality dimensions and Which dimensions of service quality should be improved primarily? A real-life case study in a public hospital is carried out to reveal how the proposed model works. The results that have been obtained from the case study show that the proposed model can be conducted easily in practice. It is also found that there is a remarkably high-service gap in the public hospital, in which the case study has been conducted, regarding the general physical conditions and food services. Originality/value The primary contribution of this study is threefold. The proposed evaluation model determines the impact levels of service quality dimensions on the service quality in practice. The proposed evaluation model prioritizes service quality dimensions in terms of their significance. The proposed evaluation model finds out the answer to the question of which service quality dimensions should be improved primarily?