• 2024-2024close
• Open Access close
• Publications close
• Other research products close
• Iskenderun Technical University Ins... close

Kütüphane ve Dokümantasyon Daire Başkanlığı Kullanıcı Hizmetleri kapsamında, Mühendislik ve Doğa Bilimleri Fakültesi kullanıcı grubuna özgü, “Mühendislik ve Doğa Bilimleri Fakültesi Konu Rehberi” hazırlanmıştır. Mühendislik ve Doğa Bilimleri orijininde gereksinim duyulacak basılı ve elektronik kaynaklar aktif linklerle desteklenmiş ve konusal olarak sınıflanmıştır.

Accurate and consistent wind speed forecasting is vital for efficient energy management and the market economy. Wind speed is non-linear, non-stationary, and irregular, so it is very difficult to forecast. There are many forecasting methods currently in use; however, selecting and developing the most appropriate method for a particular region in wind speed forecasting is still a hot topic. This study presents a new and unique neural network-based ensemble system for forecasting wind speed, which is very difficult to predict but is directly related to the power generated by wind farms for individual and different sites. With the developed ensemble model, average mean absolute error, mean absolute percentage error and root mean square error values are obtained as 0.1269, 3.074%, 0.1596 respectively. Test results demonstrate significant contributions of the proposed system compared to existing statistical, heuristic and ensemble models, indicating that the developed model is a promising alternative for wind speed forecasting models. The obtained results show that this system is an effective and useful intelligent tool that can be used by various companies and government facilities that invest and operate in intelligent wind energy technologies.

Text classification, by automatically categorizing texts, is one of the foundational elements of natural language processing applications. This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata (Wikipedia database) database and BERTbased pre-trained Named Entity Recognition (NER) models. Focusing on a significant challenge in the field of natural language processing (NLP), the research evaluates the potential of using entity and relational information to extract deeper meaning from texts. The adopted methodology encompasses a comprehensive approach that includes text preprocessing, entity detection, and the integration of relational information. Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms, such as Support Vector Machine, Logistic Regression, Deep Neural Network, and Convolutional Neural Network. The results indicate that the integration of entity-relation information can significantly enhance algorithm performance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications. Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification, the development of a Turkish relational text classification approach, and the creation of a relational database. By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification, this research aims to support the effectiveness of text-based artificial intelligence (AI) tools. Additionally, it makes significant contributions to the development of multilingual text classification systems by adding deeper meaning to text content, thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

Kütüphane ve Dokümantasyon Daire Başkanlığı Kullanıcı Hizmetleri kapsamında, Mimarlık Fakültesi kullanıcı grubuna özgü, “Mimarlık Fakültesi Konu Rehberi” hazırlanmıştır. Mimarlık orijininde gereksinim duyulacak basılı ve elektronik kaynaklar aktif linklerle desteklenmiş ve konusal olarak sınıflanmıştır.

In this study operating room scheduling (ORS) problem is addressed in multi-resource manner. In the addressed problem, besides operating rooms (ORs) and surgeons, the anesthesia team is also considered as an additional resource. The surgeon(s) who will perform the operation have already been assigned to the patients and is a dedicated resource. The assignment of the anesthesia team has been considered as a decision problem and a flexible resource. In this study, cooperative operations are also considered. A mixed integer linear programming (MILP) model is proposed for the problem. Since the problem is NP-hard, an artificial bee colony (ABC) algorithm is proposed for the problem. The solutions of the ABC are compared with the MILP model and random search.

Preliminary research findings suggest that the successful integration of advanced autonomous technologies in cargo ships could provide safer shipping and provide economical benefits. However, the fact that ships can sail with the assistance of autonomous technologies is not sufficient evidence alone to comprehend the promises of autonomous systems for maritime transport operations. Simultaneously, the entire global maritime transport system must also adapt to the advanced autonomous technologies and act interoperable with ships. In this context, this study aims to examine the areas of interoperability requirements with ports for the effective and integrated operation of autonomous cargo ships. In accordance with the purpose of the study, the operational context in which ships and ports interact is determined, and their interoperability in an autonomous maritime transport scenario is analysed. The present study contains the application of a survey questionnaire reflecting the views of experts dealing with ship and port operations. The data collected by the survey questionnaire are analysed by applying multiple regression analysis methods, utilising the IBM SPSS program. This study is in a pioneer position to define the interoperability characteristics to improve port operations to work in harmony with autonomous ships. The findings of this research are anticipated to contribute significantly to shaping the future of smart and autonomous freight transport and logistics.