Bilgisayar Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.13055/43

Listeleniyor 1 - 12 / 12

Deep-learning model for assessing difficulty in localizing impacted canines
(Elsevier, 2024) Özcan, Mustafa; Erdem, Buket; Turan, Begüm; Tokatlı, Nazlı; Şar, Çağla; Özdemir, Fulya
AIM or PURPOSE The aim of this study is to examine if an artificial intelligence algorithm can be used for identifying the bucco-palatinal position of the maxillary impacted canine from the panoramic X-rays. MATERIALS and METHOD A total of 810 panoramic x-rays were obtained from the archive of University, Faculty of Dentistry, Department of Orthodontics. X-rays included cases with unilateral/bilaterally impacted canines. We used a Convolutional Neural Network (CNN) to forecast where the impacted canines crown would be situated. CNNs excel at classifying images as they can autonomously and flexibly grasp hierarchies of features, from input images. The implementation of the proposed deep learning model has been done using the Python programming language and libraries (TensorFlow and Numpy). The dataset that was used to train the proposed model categorized into buccal, middle, and palatinal positioned samples. These samples are mainly 2D panoramic X-rays combined with clinical information about the location of the canine. The expectation from the model is to determine the location of the crown of the maxillary-impacted canine. The model's prediction of the impacted canine's location was assessed in bucco-palatinal directions. RESULTS The proposed deep learning model predicts the position of the impacted canine in the buccal, middle, or palatinal position with 68% accuracy. CONCLUSION(S) This multidisciplinary research study developed a deep learning model to automate the detection and positioning of impacted canines on panoramic dental X-rays. Lastly, further research is required to refine the model for clinical implementation and to explore its integration into routine orthodontic practice
MelanoTech: Development of a mobile application infrastructure for melanoma cancer diagnosis based on artificial intelligence technologies
(IEEE, 2024) Tokatlı, Nazlı; Bilmez, Yakuphan; Göztepeli, Gürkan; Güler, Muhammed; Karan, Furkan; Altun, Halis
This preliminary work introduces MelanoTech, a mHealth application designed and implemented to offer a user-friendly and intuitive interface for the early diagnosis of melanoma, a kind of skin cancer with significant fatality rates [1]. The application demonstrates promising performance in segmentation and classification tasks by utilizing deep learning models with Generative Adversarial Networks (GANs) for data augmentation. MelanoTech achieves a comprehensive accuracy rate of 92%, with a segmentation model accuracy rate of 93% and a lesion detection accuracy rate of 90%. Finally, incorporating data augmentation approaches based on GANs resulted in a 5% enhancement in the model’s performance. These findings highlight the capacity of MelanoTech as a dependable tool for improving the early diagnosis of melanoma and decreasing the workload of physicians in Turkish public hospitals.
GQoSMT: On guaranteeing the quality of service requirements of simultaneous multithreading processors
(IEEE, 2023) Küçük, Gürhan; Tokatlı, Nazlı; Nezir, Uğur; Pektaş, Elif Nurdan; Mete, Emrah; Gökhan Gökçek, Gülşah; Güney, Merve; Alsharif, Salwa; Esfandiari Baiat, Zahra
Guaranteeing the quality of service of a running thread on a Simultaneous Multithreading processor is one of the most challenging issues since these processors allow sharing of many datapath resources, including the Issue Queue, the Reorder Buffer, the Load/Store Queue, the branch prediction circuit, and multiple levels of the caches among multiple threads. In this study, we apply machine learning techniques to accurately predict the instant quality of service of a target thread so that we can transfer just a sufficient amount of shared resources to keep its quality of service stable at an expected level. Our test results show that our proposed prediction model gives only around 3% deviation from the target quality of service level, on average, whereas an earlier prediction model gives more than a 13% deviation.
Healthcare service accessibility path planner: Unveiling a new era of intelligent appointment management systems based on outpatient prioritizing
(IEEE, 2023) Tokatlı, Nazlı; Koçak, Muhammed Tayyip; Kırtay, Seda; Göztepeli, Gürkan; Aktaş, İbrahim Serhat; Altun, Halis
In light of increased constraints on healthcare systems, particularly as a result of the pandemic, the importance of directing patients to the appropriate healthcare departments for individualized treatment based on their health conditions has been emphasized. Numerous healthcare institutions currently employ an online booking system that enables patients to schedule appointments. However, because patient requests are the main driving force behind this process, appointments with inappropriate departments or the bypassing of primary care facilities like general practice clinics frequently occur. Many studies proposed the use of AI-based chatbots and machine learning algorithms in healthcare systems to improve clinic operations, reduce patient wait times, and predict outpatient appointment no-show rates. This paper describes the conception and implementation steps of an innovative (mhealth app) that uses open AI tools to prioritize and classify outpatients based on their symptoms. Our AI-based appointment scheduling app will decide for the outpatient either to schedule appointments with primary care facilities or direct them to the appropriate healthcare department in hospitals only when absolutely necessary, thereby nurturing a more efficient, patient-centered healthcare service.
Kullanıcı ve öğe bazlı, geniş ve derin öğrenme tabanlı seyahat öneri sistemi
(2023) Öz, Alihan; Uzun-Per, Meryem; Bal, Mert
Teknolojinin gelişmesi ile birlikte artan dijital bilgi miktarı ve internetin yaygınlaşması ile internet üzerinden ürün, hizmet, abonelik gibi ticaret işlemlerinin gerçekleştiği web sitelerinin sayısının da artması, beraberinde, müşterilere kişiselleştirilmiş ve doğru; ürün, hizmet ve abonelikleri sunmanın (önermenin) de önemini artmıştır. Müşterilere önerilerde yaygın olarak kullanılan ürün bazlı, kullanıcı tabanlı ve bu ikisinin birlikte kullanıldığı hibrit geleneksel yaklaşımlar çoğu çalışmada kullanılmaktadır. Geleneksel yaklaşımların, büyük ve seyrek veriler ile çalışma, kullanıcı ve ürün arasındaki karışık ilişkileri bulamama ve soğuk başlangıç (cold start) gibi problemlerinin üstesinden gelmek, derin ve geniş öğrenme sistemlerinin kullanımı ile mümkün olmuştur. Bu çalışma kapsamında, öncelikle derin ve geniş sinir ağlarına ve bunların seyahat öneri sistemlerindeki uygulamalarına kapsamlı bir bakış açısı sunulmuş ve en popüler öneri algoritmaları olan Google'ın Geniş ve Derin Algoritması ve Facebook'un Deep Learning Recommendation Model (DLRM) algoritmasına yer verilmiştir. Ardından, geniş ve derin öğrenme yaklaşımı ile kullanıcı ve ürün özelliklerinin kategorik olanlarının gömme işlemi uygulanarak, nümerik veriler ile modele beslendiği yeni bir seyahat öneri sistemi oluşturulmuştur. Önerilen yöntem gerçek bir seyahat acentesi şirketinin veri seti üzerinde uygulanmıştır. Sonuçta, kullanıcılara verilen en iyi beş öneride, %82.37 doğruluk oranı yakalanmıştır.
Testing the performance of feature selection methods for customer churn analysis: Case study in B2B business
(Springer, 2023) Sancar, Semanur; Uzun-Per, Meryem; García Márquez, Fausto Pedro; Jamil, Akhtar; Eken, Süleyman; Hameed, Alaa Ali
Churn analysis has recently become one of the favorite topics of marketing teams with the development of machine learning models. This study aims to discover the most suitable feature selection (FS) model for churn analysis by using the databases of BiletBank, a business-to-business (B2B) company. It was found that some categorical data such as agency type and currency used by customers, along with periodic flight sales data, are also meaningful features for churn analysis in the BiletBank customer portfolio. This feature selection study in the database will be a source for future churn analysis studies.
Digital health navigator: Preliminary work on a personal health assistant software for all health literacy level users in Turkey
(İzmir Ekonomi Üniversitesi, 2023) Tokatlı, Nazlı; Kömür, Fatma Nur; Koçak, Muhammed Tayyip; Kırtay, Seda; Göztepeli, Gürkan; Gül, Beyza; Altun, Halis
Today's digital health terminology is actually advanced med-ical technologies that include computer-assisted therapy, smartphone apps, and wearable technologies. These technologies offer significant po-tential for improving accees to immediate medical care, efficiency, clinical effectiveness, and personalization of many health problem therapies. In this paper. we will elobareate on the preliminary design steps of a per-sonalized health assistant application project (PHAS). The proposed ap-plication can be classified as a mobile health app and not a telemedicine application. The idea behind this application is to reduce the physicians' workload in hospitals while providing health care to the comm y with different health literacy levels by easily using the application when gen-eral assistance about any health issues or an overall health and wellness improvement is required.
Feature selection in customer churn analysis: Case study in B2B business
(Institute of Electrical and Electronics Engineers Inc., 2022) Sancar, Semanur; Uzun-Per, Meryem
Customer churn analysis is one of the machine learning applications that has become a hot topic in businesses with the developing technology. Since the performance of fore-casting algorithms is directly affected by the abundance of data, the literature for B2C businesses has been developed faster. In B2B businesses, on the other hand, since customer dynamics are slightly different and the number of customers is not as high as in B2C, data mining studies have been carried out less frequently. Within the scope of BiletBank R&D studies, it is aimed to analyze the customer loss of BiletBank, a B2B company. In line with the customer loss analysis target, the categorical features of BiletBank customers, such as the city they are in, and their periodic interactions with the BiletBank system, have been converted into a data set. In this study, the evaluation of the features in the data set was carried out to create a source for customer loss analysis. Evaluation of features has been implemented by establishing nine different models, including statistical, wrapper, and embedded methods. It is aimed that the feature importance determined as a result of this study will be used in the customer churn analysis studies to be carried out from now on.
On the big data processing algorithms for finding frequent sequences
(Wiley, 2023) Can, Ali Burak; Zaval, Mounes; Uzun-Per, Meryem; Aktaş, Mehmet Sıddık
Sequential pattern mining algorithms extract trendy sequence appearances insideordered transactional datasets such as market basket datasets. There is a lack ofresearch employing big data processing techniques to locate frequent sequences onlarge-scale datasets. Furthermore, there is a need for optimized sequential patternmining algorithms that run on ordered one-dimensional sequences. We also observe alack of sequential pattern search studies in the literature, where the focus is centeredaround multi-dimensional data sequences. Existing approaches that deal with orderedone-dimensional datasets suffer from scalability issues as the amount of data to beanalyzed is enormous. This research investigates the big data processing techniquesused to find frequent sequences in large-scale datasets. It also proposes a scalablesequence pattern mining algorithm called Sequential Pattern Acquisition by ReducingSearch Space (SPARSS) designed for distributed data processing systems that effi-ciently handle large datasets containing sequential one-element data. It introducesa prototype implementation of SPARSS and provides information on the SPARSS’smemory and time requirements, which were calculated as part of experimental stud-ies on a real-world dataset. The results confirm our expectations and demonstrateSPARSS’s superior scalability and run-time efficiency compared to other distributedalgorithms.
A novel sequential pattern mining algorithm for large scale data sequences
(Springer, 2022) Can, Ali Burak; Uzun-Per, Meryem; Aktaş, Mehmet Sıddık
Sequential pattern mining algorithms are unsupervised machine learning algorithms that allow finding sequential patterns on data sequences that have been put together based on a particular order. These algorithms are mostly optimized for finding sequential data sequences containing more than one element. Hence, we argue that there is a need for algorithms that are particularly optimized for data sequences that contain only one element. Within the scope of this research, we study the design and development of a novel algorithm that is optimized for data sets containing data sequences with single elements and that can detect sequential patterns with high performance. The time and memory requirements of the proposed algorithm are examined experimentally. The results show that the proposed algorithm has low running times, while it has the same accuracy results as the algorithms in the similar category in the literature. The obtained results are promising.
Scalable recommendation systems based on finding similar items and sequences
(Wiley, 2022) Uzun-Per, Meryem; Gürel, Ahmet Volkan; Can, Ali Burak; Aktaş, Mehmet S.
The rapid growth in the airline industry, which started in 2009, continued until the COVID-19 era, with the annual number of passengers almost doubling in 10 years. This situation has led to increased competition between airline companies, whose profitability has decreased considerably. They aimed to increase their profitability by making services like seat selection, excess baggage, Wi-Fi access optional under the name of ancillary services. To the best of our knowledge, there is no recommendation system for recommending ancillary services for airline companies. Also, to the best of our knowledge, there is no testing framework to compare recommendation algorithms considering their scalabilities and running times. In this paper, we propose a framework based on Lambda architecture for recommendation systems that run on a big data processing platform. The proposed method utilizes association rule and sequential pattern mining algorithms that are designed for big data processing platforms. To facilitate testing of the proposed method, we implement a prototype application.We conduct an experimental study on the prototype to investigate the performance of the proposed methodology using accuracy, scalability, and latency related performance metrics. The results indicate that the proposed method proves to be useful and has negligible processing overheads.
Big data testing framework for recommendation systems in e-science and e-commerce domains
(IEEE, 2021) Uzun-Per, Meryem; Can, Ali Burak; Gürel, Ahmet Volkan; Aktaş, Mehmet S.
Software testing is an important process to evaluate whether the developed software applications meet the required specifications. There is an emerging need for testing frameworks for big data software projects to ensure the quality of the big data applications and satisfy the user requirements. In this study, we propose a software testing framework that can be utilized in big data projects both in e-science and e-commerce. In particular, we design the proposed framework to test big data-based recommendation applications. To show the usability of the proposed framework, we provide a reference prototype implementation and use the prototype to test a big data recommendation application. We apply the prototype implementation to test both functional and non-functional methods of the recommendation application. The results indicate that the proposed testing framework is usable and efficient for testing the recommendation systems that use big data processing techniques.

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