Lojistik sektörü dünyada ve Türkiye’de giderek büyümekte ve sektörün potansiyeli zamanla daha iyi anlaşılmaktadır. Lojistik sektörünün gelişime oldukça açık olduğu, teknoloji ile ortaya çıkan yeniliklere ayak uydurmak zorunda olduğu bilinmektedir. İşletmeler bu yeniliklere ayak uydurarak, rekabette başarılı olmaya çalışmaktadır. Endüstri 4.0 özellikle lojistik gibi rekabetin ön planda olduğu sektörleri etkisi altına almıştır. Yapılan son araştırmalarda yapay zeka tekniklerinin kullanımında büyük oranda artış olduğu görülmektedir. Yapay zekanın lojistik sektöründe kullanılması sonucunda özellikle işleyiş ve dinamiklerde değişiklikler oluşmaya başlamıştır. Yapay zeka, insan zekasının fizyolojik ve nörolojik yapısını çeşitli teknolojiler yardımı ile modelleyerek makinelere aktarmaktadır. Yapay zeka ile birlikte ortaya çıkan sürücüsüz araçlar, depolama ve raflarda kullanılan robotlar, sistem içerisinde büyük verilerin rahatlıkla kullanılması gibi seçenekler lojistik sektöründeki hataların en aza indirgenmesini sağlamaktadır. Lojistik sektöründe yapay zeka kullanımı sayesinde işletmeler daha verimli işler ortaya koymaktadır. Yapılan bu çalışmada, lojistik sektöründe kullanılan yapay zeka ve makine öğrenmesi uygulamalarının geniş bir perspektif ile incelenmesi amaçlanmıştır. Çalışmada önce yapay zeka ve makine öğrenimi kavramları açıklanmış ardından endüstri ve lojistik kavramlarına değinilerek lojistikte kullanılan yapay zeka ve makine öğrenmesi uygulamalarına yer verilmiştir. Küresel lojistik ve tedarik zinciri yönetimi konusunda yapay zekanın günden güne kendini geliştirdiği ve lojistik süreçleri kolaylaştırdığı görülmektedir.

Because of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery to survive against their competitors. For this reason, logistics play a significant role in the warehousing, shipments, and transportation of the products. Therefore, the high utilization of resources can improve the profit margins and reduce unnecessary storage or shipping costs. One significant issue in shipments is the Pallet Loading Problem (PLP) which can generally be solved by seeking to maximize the total number of boxes to be loaded on a pallet. In many previous studies, various solutions for the PLP have been suggested in the context of logistics and shipment delivery systems. In this paper, a novel two-phase approach is presented by utilizing a number of Machine Learning (ML) models to tackle the PLP. The dataset utilized in this study was obtained from the DHL supply chain system. According to the training and testing of various ML models, our results show that a very high (>85%) Pallet Utilization Volume (PUV) was obtained, and an accuracy of >89% was determined to predict an accurate loading arrangement of boxes on a suitable pallet. Furthermore, a comprehensive analysis of all the results on the basis of a comparison of several ML models is provided in order to show the efficacy of the proposed methodology.

Various studies have been conducted to reveal and analyse tissues from humans with distinct properties. The interpretation of human facial tissues was the subject of a few of these investigations. The aim of this study was to look at the energy ratio...

Background: This study aims to identify predictors of postoperative atrial fibrillation in coronary artery bypass grafting patients using routinely collected preoperative tests. Methods: Between January 2020 and December 2023, a total of 50 patients with postoperative atrial fibrillation (POAF group; 39 males, 11 females; mean age: 65.9±8.3 years; range, 38 to 77 years) and 50 without postoperative atrial fibrillation (non-POAF group; 41 males, 9 females; mean age: 61.8±10.0 years; range, 41 to 81 years) were randomly selected from a group of patients undergoing two or three-vessel coronary artery bypass grafting. We analyzed preoperative laboratory, demographic and intraoperative data using machine learning models. Results: The overall incidence of postoperative atrial fibrillation was 21.69%. The three most effective biomarkers were magnesium, total iron binding capacity, and albumin, respectively. A total of 2.0 mg/dL value of magnesium was identified as a threshold value. Magnesium values below 2.0 mg/dL were considered atrial fibrillation-positive, accounting for 25% of the dataset. Total iron binding capacity values higher than 442 µg/dL were considered atrial fibrillation-positive, accounting for 12% of the dataset. The threshold value for albumin was 29 g/dL, and patients with values under this value were considered atrial fibrillation-positive, accounting for 4% of the dataset. Conclusion: Machine learning models demonstrate encouraging results in identifying risk factors for many entities. It is of utmost importance to establish a ranking among risk factors and determine threshold values to support clinicians in decision making. This is our first experience with machine learning in this patient group after cardiac surgery. Further studies are warranted to confirm these data.

Background: Cushing syndrome (CS) is a rare endocrine disorder characterized by excessive secretion of glucocorticoids, leading to a variety of clinical manifestations, comorbidities, and increased mortality despite treatment. Despite advances in imaging modalities and biochemical testing, the diagnosis and management of CS remains challenging. Several tests are used to confirm the diagnosis of CS, including urinary free cortisol measurements, dexamethasone suppression tests (1 mg, 2 mg, and 8 mg), and nocturnal salivary cortisol measurements. However, each of these tests has some limitations, making the diagnosis of CS. Methods: In this paper, we explore the potential of state-of-the-art machine learning algorithms as a clinical decision support system for analyzing and classifying CS. Our aim is to use advanced machine learning methods to analyze the accuracy rates of diagnostic tests and identify the most sensitive tests for diagnosing CS. Results: In this study, we performed binary classification based on data from 278 patients with CS (CS+) and 220 healthy patients (CS-). We developed a linear mathematical model with high predictive ability, achieving a classification accuracy of 97.03% and a Kappa value of 94.05%. The correlation graph shows that CS has strong positive relationships with 2 mg (78.8%), 1 mg (76.9%), and mc (72.1%), and moderate positive correlations with 8 mg (45%) and saliva (45.4%). In contrast, gender has almost no correlation with CS, so it was removed from the dataset. As a result, the model achieves an overall classification accuracy of 97.03%. Finally, we converted the linear model into a mobile application for use by specialist doctors in the field of endocrinology. Conclusion: Traditional diagnostic methods can be time-consuming and require specialized medical expertise. Recently, advances in machine learning and mobile technology have opened new avenues for improving diagnostic accuracy and accessibility. This study explores the integration of machine learning algorithms into a mobile application designed to assist healthcare professionals and patients in the diagnosis of CS.

It is an indisputable fact that energy plays a big role in the development of countries. Electrical energy has a great share in the development. Electricity is a secondary energy source, i.e. it is obtained by transforming primary energy sources. Although the desired level has not yet been reached, Turkey’s installed power has increased by years and a wide variety of energy sources such as coal, oil, natural gas, hydroelectric energy, wind, solar and other renewable energy sources are used in electricity generation. At this point, it is observed that the share of renewable energy sources in total electricity generation has increased from year to year. It should be underlined that this increase is very important for the country’s economy. In this study, Turkey’s electricity generation by sources for the years 2020 and 2021 was predicted with artificial neural network (ANN) and bidirectional long short - term memory (BLSTM) methods using the data for electricity generation by sources in the years 2010-2019. The share of electricity generated from renewable energy sources in total electricity generation for 2020 by ANN and BLSTM methods was calculated as 18.08% and 18.6% respectively. For 2021, the share of electricity generated from renewable energy sources in total electricity generation was calculated as 21.95% and 21.68% respectively. These results show that the share of electricity generated from renewable energy sources in total electricity generation will increase. Finally, suggestions were made on what kind of roadmap should be followed in the field of investments in renewable energy resources.