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Some modern methods related to advanced technologies in teaching STEM specialists
No full textTIM 2022 Physics Conference -- 23 November 2022 through 25 November 2022 -- Timisoara -- 199897In addition to several societal and individual impacts, the pandemic of COVID-19 has greatly affected educational systems everywhere. The education in those universities that prepare STEM specialists like engineers, physicists, chemists, computer scientists, etc., has faced new requirements and problems. The solutions found are closely linked to new technologies, curricula innovation, and rethinking of the organizational structure of higher education. In this framework, different institutions reacted differently, working in an international environment and exchanging ideas and experiences. In the ERASMUS+ project "Applying some advanced technologies in teaching and research, in relation to air pollution,"faculties from four universities from Romania (Craiova), Bulgaria (Plovdiv), Slovakia (Banská Bystrica) and Turkey (Adana) have tried to draw some positive lessons from the pandemic, on how to improve the quality of teaching in an online or hybrid environment. Some trends successfully applied in students' education will be presented and outlined as the framework of a conceptual educational model with an enhanced presence of modern educational technologies. © 2024 Author(s).European Commission, E
DEVELOPMENT AND ASSESSMENT OF A POST-OCCUPANCY EVALUATION SCALE FOR SUSTAINABLE OFFICE ENVIRONMENTS INSIGHTS FROM THE FNN SUSTAINABILITY CENTRE
No full textThis study explores the relationship between users and the built environment through a post -occupancy evaluation (POE) conducted at the FNN Sustainability Centre, a noteworthy sustainable building in the region. The study involved a comprehensive approach, encompassing site visits, managerial interviews, and staff surveys. To establish a robust evaluation framework, a scale was developed by analysing pertinent literature, and indicators were identified to gauge various aspects of the building's performance. Throughout the scale development process, the SPSS data analysis program was used, and expert opinions were solicited to ensure a rigorous and comprehensive methodology. Evaluation categories included lighting, acoustics, climatic comfort and indoor air quality, use and comfort of systems, quality of space and perception, awareness of sustainability and productivity. The building emerged as a physically and psychologically conducive workplace that heightened employee awareness of sustainability. Specific concerns were identified, such as noise disturbance for open -office workers and glare -related issues, which serve as valuable feedback for potential adjustments
Automated Depression Detection from Tweets: A Comparison of NLP Techniques
No full text8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423This paper aims to classify suicidal ideation as a symptom of depression from social media posts by applying the state-of-the-art classification model BERT (Bidirectional Encoder Representations from Transformers) and three traditional machine learning algorithms for binary classification. Since depression is one of the most prevalent mental health disorders amongst psychiatric disorders, the authors intended to present an experimental analysis of the machine learning classifier results as a comparison of novel depression detection techniques. We utilized undiagnosed user posts from Twitter as our dataset and tested the fine-tuned BERT model by applying hold-out and 10-fold cross-validation techniques. Since the dataset is highly unbalanced, Support Vector Machine (SVM), Naive Bayes, and Random Forest algorithms were employed on the same dataset with and without the oversampling method SMOTE (Synthetic Minority Oversampling Technique). The results demonstrate that traditional machine learning classifiers cannot infer sentiment from data containing various linguistic cues, such as depression symptoms. On the other hand, the state-of-the-art model BERT achieves 99.29% and 99.56% macro and micro-F-measure values, respectively, surpassing traditional machine learning algorithms in terms of these metrics. As a robust solution to depression detection from textual data, the BERT model is more trustworthy than the traditional machine learning classifiers to detect specific cues related to depression and similar mental disorders. This study contributes to the relevant research areas of natural language processing by indicating the performance difference between the BERT model and several traditional machine learning algorithms as a generalized approach for classification tasks. © 2024 IEEE
Time Series Installed Capacity Forecasting with Deep Learning Approach for Türkiye
No full textDeep learning methods have been developed to solve different problems due to the complex nature of real-world problems. Accurate future forecasting of a country's installed capacity is also crucial for developing a good energy sustainability strategy for the country. In this paper, three different time series forecasting methods are used for forward forecasting of installed capacity: Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Installed power values for the years 1923-2021 were used in the study. Then, future forecasts are made until 2030. The GRU model achieved the best RMSE in the testing phase compared to the LSTM and CNN models. Although CNN is successful during training, it has a higher RMSE during testing compared to GRU. While all models predict a potential increase in electricity capacity by 2030, GRU and LSTM predict a more significant increase up to this point compared to CNN
Retraction Note: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection (Soft Computing, (2023), 27, 9, (5521-5535), 10.1007/s00500-022-07798-y)
No full textThe publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher no longer has confidence in the results and conclusions of this article. The authors disagree with this retraction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024
Türkçe kimlik avı web sitelerinin etkin tespiti için derin öğrenme ve makine öğrenmesi sınıflandırıcılarını birleştiren yeni, iki aşamalı bir yaklaşım
No full textLisansüstü Eğitim Enstitüsü, Siber Güvenlik Ana Bilim DalıSon yıllarda internet hızının artması ve internete bağlı cihaz sayısının da paralel olarak artması ile birlikte online dolandırıcılık da önemli ölçüde artış göstermiştir. Saldırganlar, WhatsApp, e-posta, SMS, mobil bildirimler ve sosyal medya mesajları gibi platformları kullanarak dikkat çekici, ilgi çekici veya korkutucu içerikler yaymaktadırlar. Kullanıcıları bu içeriklere etkileşimde bulunmaya ve gömülü bağlantılara tıklamaya teşvik ederek, kötü niyetli aktörler kullanıcıları otantik sitelere çok benzeyen sahte web sitelerine yönlendirir ve bu şekilde kullanıcıların güvenli bilgilerini ele geçirir veya farklı yollarla menfaat temin eder. Bu tür aldatmaca işlemleri için hazırlanan ve "phishing" siteleri olarak adlandırılan bu kötü niyetli web sayfalarının, kullanıcıların erişiminden önce mobil uygulamalar veya tarayıcılar tarafından tespit edilmesi son derece önemlidir. Bu çalışma, oltalama sitelerini tanıma konusunda %98,4'lük bir başarı oranına ulaşmak için iki aşamalı bir yaklaşım önermektedir. Kullanılan veri seti, Ulusal Siber Olaylara Müdahale Merkezi'nin (USOM) oltalama siteleri listesi ve meşru alan adlarından oluşmaktadır. Veri seti, Dataset1 ve Dataset2 olmak üzere iki alt küme halinde ayrılmıştır. Dataset1, derin öğrenme tabanlı bir yapay zeka modelini eğitmek için kullanılarak, eğitim sonucunda %92'lik bir doğruluk değeri elde etmiştir. Dataset2 ise derin öğrenme tabanlı modelin bir site için verdiği oltalama puanının yanında yine o web sitesine ilişkin ek özellikleri içeren ve ikili sınıflandırma için bir makine öğrenimi modelini kullanan modelin hazırlanmasında kullanılmıştır. Yapılan testler, önerilen bu yaklaşımın bir web sitesi için %98,4'lük bir doğruluk puanı ile oltalama sitesi olup olmadığına dair tahmin yapabildiğini göstermektedir. Anahtar Kelimeler: Phishing, Online Dolandırıcılık, Siber Saldırı, Makine öğrenmesi, Derin öğrneme, Zararlı URLWith the increase in internet speed and the parallel rise in the number of internet-connected devices, online fraud has exhibited a significant surge in recent years. Attackers exploit platforms such as WhatsApp, email, SMS, mobile notifications, and social media messages to disseminate content that is attention-grabbing, intriguing, or fear-inducing. By inducing users to interact with these contents and click on embedded links, these malevolent actors redirect users to counterfeit websites that closely mimic authentic ones, thereby obtaining users' confidential information or engaging in various forms of deception. Commonly referred to as "phishing" sites, these malicious web pages are often used for such deceptive operations. Consequently, it is of paramount importance that mobile applications or browsers possess the capability to identify such harmful websites even before users access them. This study employs a two-stage approach to achieve a 98.4% success rate in identifying malicious sites. The dataset used consists of a list of malicious sites from the National Cyber Incident Response Center (USOM) alongside legitimate domain names. The dataset is divided into two subsets, namely Dataset1 and Dataset2. Dataset1 is employed to train a deep learning-based artificial intelligence model, which yields an accuracy rate of 92% upon completion of training. The websites within Dataset2 are subjected to the deep learning model in the initial stage to acquire phishing scores. Subsequently, by incorporating additional features pertaining to each website and employing a machine learning model for binary classification, the second stage of training facilitates the culmination of the ultimate outcome. Test results demonstrate the capacity to predict phishing incidents with a 98.4% accuracy score for a given website. Keywords: Online Fraud, Cyber Attack, Machine learning, Deep learning, Malicious UR
Enhancing gust load alleviation performance in an optimized composite wing using passive wingtip devices: Folding and Twist approaches
No full textThis paper introduces an innovative numerical method for the design and optimization of high-aspect-ratio composite wings equipped with passive control systems, specifically, Folding WingTip (FWT) and Twist WingTip (TWT) devices. The aim is to enhance Gust Load Alleviation (GLA) performance in the baseline wing. Recent numerical studies have indicated that the inclusion of spring devices and wingtip modifications can offer additional benefits in alleviating gust loads during flight. The baseline wing is designed using a comprehensive multi-disciplinary optimization framework, taking into account aerostructural constraints and exploiting the anisotropic properties of composite materials. The proposed methodology integrates Finite Element (FE) software, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses, and Particle Swarm Optimization (PSO). This method, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, facilitated the streamlined design of composite wings with optimized aeroelastic and structural performance. The paper is divided into two main parts. Part 1 introduces a Multidisciplinary Design Optimization (MDO) approach for high-aspect-ratio composite wings, leading to the development of a baseline wing model. Part 2 evaluates the effectiveness of the FWT and TWT devices in alleviating gust loads on the baseline wing, with a focus on the Root Bending Moment (RBM) as a critical criterion for comparison. In wingtip modeling, geometrical nonlinearity is incorporated, and elastic trim is adjusted in each iteration to accommodate shape changes under load and aerodynamic panel movement is synchronized with structural adjustments.Research Council of Turkey [TBIbull;TAK, 220N396, TBIbull;TAK 2219]; EPSRC Impact Acceleration AccountThis study has been supported by the Scientific and Technologicalr Research Council of Turkey (TUBIcenter dotTAK, Project No. 220N396 and TUBIcenter dotTAK 2219 program) . The authors gratefully acknowledge the support of this study. Hamed Haddad Khodaparast acknowledges the support received from the EPSRC Impact Acceleration Account
How the powerful maintained their power: land, violence and identity in fin de siecle Palu
No full textThis article is set in the environs of the Eastern Anatolian town of Palu at the turn of the twentieth century. At the heart of this investigation is a puzzle: how did the local elite manage to maintain their power in the face of first Tanzimat (1839-1876) and then Hamidian centralization (1876-1908)? Based on the study of a range of primary sources, it appears that the local elites were able to 'use' the Armenian Question, and the fears of the central authorities, to their advantage. The elites increasingly presented themselves as 'loyal Muslims' in the face of supposedly 'seditious Armenians' to maintain control of the land. In addition to British Foreign Office documents, our article relies primarily on a voluminous legal file compiled from the catalogues of the Ottoman Archives, Istanbul composed by different segments of the region's population
How do individuals with autism participate in work life? A study on inclusive employability
No full textPurpose - In studies considering the employment of individuals with autism, the organisational context - which consists of the behaviours and attitudes of employees - has frequently been neglected. This study investigates the employment of workers with autism, who have an intellectual disability (AID) in Turkiye. The study aims to understand the perspectives of managers and co-workers with regard to the employment of individuals with AID. Design/methodology/approach - The authors conducted interviews with 23 people who were the co-workers, managers and parents of workers with AID. They also reviewed performance documents concerning employees with AID and analysed the data using qualitative content analysis. Findings - The employment of individuals with AID has caused concern amongst employees within organisations. However, training activities have raised awareness of autism amongst those employees. This new awareness has overcome initial negative judgements about the employment of individuals with AID, turning these instead into positive ones. Thus, social interaction between workers with AID and their co-workers has increased. Practical implications - This research provides evidence of the positive impact of employees with AID on companies and shows that employing individuals with AID in inclusive contexts improves their quality of life. It also provides guidance for the design of training programmes for employees and the adaptation processes of people with disabilities in the workplace. Originality/value - This study emphasises the role of the organisational context in the successful employment of people with AID in supported employment settings. It could contribute to changing attitudes and negative expectations and guide interventions in these contexts
Iran's axis of resistance through the lens of ontological security
No full textUnlike traditional approaches that analyse an actor's foreign policy by focusing on the search for physical security and material interests, ontological security advocates a more nuanced approach that considers identity politics, regime security, issues of legitimacy, and emotional motives. This article aims to examine Iran's Axis of Resistance from the perspective of ontological security, without rescinding the possibility of power struggles or ideological motivations in influencing Iran's foreign policymaking process. This study posits that the Axis of Resistance embodies a profound meaning and overarching significance for Iran that transcends mere considerations of physical security or sectarian-ideological inclinations. The pursuit of the Axis of Resistance by Iran serves to provide the regime with ontological security, achieved through the utilisation of national narratives, the establishment of routines, concerns pertaining to domestic order, and the incorporation of emotional underpinnings of the Islamic regime. The existential significance that Iran ascribes to the Axis of Resistance is of crucial importance in maintaining this paradigm, despite all its costs.This article is derived from the doctoral dissertation of Dr Cingoz, which was completed at the Department of International Relations at Akdeniz University under the supervision of Dr ozkan and the guidance of the thesis committee, which included Dr Alkan and Dr Izol