Bilgisayar Mühendisliği Bölümü Koleksiyonu
Bu koleksiyon için kalıcı URIhttps://hdl.handle.net/20.500.12573/203
Gözat
17 Sonuçlar
Search Results
Konferans Nesnesi Atıf - WoS: 1Atıf - Scopus: 1A New Method to Identify Affected Pathway Subnetworks and Clusters in Colon Cancer(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019-09) Goy, Gokhan; Yazici, Miray Unlu; Bakir-Gungor, BurenNowadays new technological developments that play an important role in the production of big data have brought about the interpretation, sharing and storage of data related to complex diseases. Combining multi-omic data in different molecular levels is potentially important for understanding the biological origin of complex diseases. One of these complex diseases is cancer of different types, which has one of the highest causes of death worldwide. The integration of multiple omic data in the framework of a comprehensive analysis and identification of relevant pathways contribute to the development of therapeutic approaches related to disease. In this study, RNA and methylation data (genes and p values) of colon adenocarcinoma were obtained from TCGA data portal and combined with Fisher's method. While protein subnetworks affected by the disease were identified by using subnetwork algorithm, pathways related to the disease and genes associated with these pathways were determined by functional enrichment analysis. Using gene-pathway relationship matrix, kappa scores of pathways were determined by similarity calculation. In this way, the pathways were clustered according to the hierarchically optimal number, as a result, the most important pathway clusters and related genes that are effective in disease formation identified.Makale Atıf - WoS: 42Atıf - Scopus: 55CBI4.0: A Cross-Layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0(Elsevier, 2021) Faheem, Muhammad; Butt, Rizwan Aslam; Ali, Rashid; Raza, Basit; Ngadi, Md Asri; Gungor, Vehbi CagriIndustry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.Konferans Nesnesi Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2020) Goy, Gokhan; Kolukisa, Burak; Bahcevan, Cenk; Gungor, Vehbi CagriWith the developing technology in every fields, a competitive marketing environment has been arised In this competitive environment analyzing customer behavior has become vital In particular, the ability to easily change any service provider has become vet) , critical for the company to continue its existence At the same time, the amount of financial resources spent on retaining instituters much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities In this study. we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value In this context the aunt successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936.Konferans Nesnesi Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets(Springer Verlag service@springer.de, 2018) Cetiner, Erkan; Güngör, Vehbi Çağrı; Kocak, TaskinHybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches. © 2018 Elsevier B.V., All rights reserved.Makale Atıf - WoS: 2Atıf - Scopus: 10Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm(SPRINGER, 2023-10-24) Alshakree, Firas; Akbas, Ayhan; Rahebi, JavadPalm print identification is a biometric technique that relies on the distinctive characteristics of a person’s palm print to distinguish and authenticate their identity. The unique pattern of ridges, lines, and other features present on the palm allows for the identification of an individual. The ridges and lines on the palm are formed during embryonic development and remain relatively unchanged throughout a person’s lifetime, making palm prints an ideal candidate for biometric identification. Using deep learning networks, such as GoogLeNet, SqueezeNet, and AlexNet combined with gray wolf optimization, we achieved to extract and analyze the unique features of a person’s palm print to create a digital representation that can be used for identification purposes with a high degree of accuracy. To this end, two well-known datasets, the Hong Kong Polytechnic University dataset and the Tongji Contactless dataset, were used for testing and evaluation. The recognition rate of the proposed method was compared with other existing methods such as principal component analysis, including local binary pattern and Laplacian of Gaussian-Gabor transform. The results demonstrate that the proposed method outperforms other methods with a recognition rate of 96.72%. These findings show that the combination of deep learning and gray wolf optimization can effectively improve the accuracy of human identification using palm print images.Makale Hydroponic Agriculture with Machine Learning and Deep Learning Methods(Gazi Mühendislik, 2023) Bulut,Nurten; Hacıbeyoğlu, MehmetIn the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.Konferans Nesnesi Identification of Shared Pathways Among Immune Related Diseases Utilizing Active Subnetworks(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2020-09) Eryilmaz, Mahmut Kaan; Kuzudisli, Cihan; Gungor, Burcu BakirDifferent, but related diseases often contain shared symptoms indicating the presence of possible overlaps in underlying pathogenic mechanisms. The identification of the shared pathways and related factors across these diseases helps to better understand the causes of these diseases, to prevent and treat these diseases. In this study, using immune-related diseases, we proposed a new method on how to compare the development mechanisms of related diseases based on biological pathways. Following the developments in genomic technologies, the genotyping gets cheaper and easier, and hence genome-wide association studies (GWAS) emerged. By this means, via studying big-sized case-control groups for a specific disease, potential genetic variations, single nucleotide polymorphisms (SNPs) could he identified. With the help of these studies, in which around a million of SNPs are scanned, the variations and genes that could have a role in specific disease development could be detected. In this study, via using available GWAS datasets and human protein-protein interaction network, and via detecting active subnetworks and affected pathways, seven immune related diseases are analyzed. Via investigating the similarities among the identified pathways for related diseases, we aim to define the underlying pathogenic mechanisms, and hence to contribute to the elucidation of disease development mechanisms and to the drug repositioning studies.Konferans Nesnesi In-silico Identification of Papillary Thyroid Carcinoma Molecular Mechanisms(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019-04) Ersoz, Nur Sebnem; Guzel, Yasin; Bakir-Gungor, BurcuRepresenting approximately 70% to 80% of thyroid cancers, papillary thyroid cancer (PTC) is the most common type of thyroid cancers. PTC is seen in all age groups, but it is seen more frequently in women than in men. Detection of biomarker proteins of papillary thyroid cancinoma plays an important role in the diagnosis of the disease. In this study, we aim to find target genes and pathways that are associated with papillar thyroid carcinoma, by integrating different bioinformatics methods. For this purpose, usingin-silico methodologies, candidate genes and pathways that could explain disease development mechanisms are identified. Throughout this study, firstly we identified differentially expressed genes as the amount of their protein product differ between patient and healthy groups. Secondly, by using active subnetworks search algorithms, topologic analyses and functional enrichment tests, candidate proteins,which could be thought as PTC biomarkers, and affected pathways are identified.Araştırma Projesi Kablosuz Sualtı Algılayıcı Ağlarında Katmanlar Arası İletişim Ve Fırsatçı Spektrum Erişimi(TUBİTAK, 2018) Güngör, Vehbi Çağrı; Tuna, GürkanDünyamızın üçte ikisinden fazlası sularla kaplıdır. Denizlerden, göllerden ve_x000D_ nehirlerden oluşan sualtı dünyası doğal kaynaklar (petrol, doğalgaz ve değerli mineraller)_x000D_ bakımından oldukça zengin olup insanoğlu tarafından henüz tam olarak keşfedilememiştir._x000D_ Son yıllarda, bilimsel, çevresel, ticari ve askeri uygulamalarda kullanılmak üzere kablosuz_x000D_ sualtı algılayıcı ağların geliştirilmesi ve gerçekleştirilmesi noktasında endüstride ve_x000D_ akademide olağanüstü artan bir hızda ilgi olmuştur. Günümüzde sualtı algılayıcı ağlarının_x000D_ deprem izleme, denizbilim veri toplanması, felaket yönetimi, çevresel kirliliğin gözlemlenmesi,_x000D_ güvenli gemi seyri, çoklu ortam taktik izleme vb. alanlarda çeşitli uygulamaları bulunmaktadır._x000D_ Bununla birlikte, sualtı akustik ortamı güvenilir ve etkin kablosuz iletişim için ciddi zorluklar_x000D_ yaratmaktadır. Bu bağlamda, güvenilir ve etkin sualtı iletişimin sağlanması için özgün bir_x000D_ iletişim sistemi gerekmektedir._x000D_ Bu projede, kablosuz sualtı algılayıcı ağlarının ortak problemleri olan yayılım_x000D_ gecikmesinin yüksek ve değişken olması, sualtı kanal kapasitesinin yere, zamana ve_x000D_ frekansa bağlı olarak ciddi şekilde değişiklik göstermesi ve kablosuz sualtı algılayıcı_x000D_ düğümlerinin çok sınırlı enerjiye sahip olması gibi problemlerin adreslenmesi için konum_x000D_ farkında Katmanlar arası İletişim ve Fırsatçı Spektrum Erişim (Kİ-FSE) sistemi_x000D_ önerilmektedir. Geliştirilen Kİ-FSE sistemi kaynakları kısıtlı sualtı elemanları için geleneksel_x000D_ iletişim katman modelinde uygulama katmanından fiziksel katmana kadar iletişim_x000D_ katmanlarının yükünü azaltacak ve performanslarını geliştirecek tam bir katmanlar arası_x000D_ çözümdür. Ayrıca, Kİ-FSE sistemi sualtı ortamında etkin spektrum kullanımını sağlamak için_x000D_ fırsatçı spektrum erişim tekniklerinden faydalanmaktadır._x000D_ Genel olarak, bu projenin nihai sonucu sualtı ortamları için özgün katmanlar arası ve_x000D_ fırsatçı spektrum erişim esasına dayanan iletişim protokollerinin geliştirilmesi için gerekli_x000D_ metotların ve temel kavramların ortaya konmasıdır. Sonuç olarak; bu projenin kariyer ve_x000D_ eğitimsel faydalarına ilaveten, bu projenin sonuçlarıyla mümkün kılınabilecek sağlam ve_x000D_ geniş ölçekli sualtı algılayıcı ağları sualtı dünyasının bilimsel, çevresel, ticari, askeri, vb._x000D_ amaçlar için kapsamlı keşfini başarmayı mümkün kılacak ve sualtında bulunan yeni doğal_x000D_ kaynakların (petrol, doğal gaz, vb.) sualtı algılayıcı ağları tarafından keşfedilmesine önayak_x000D_ olacaktırKonferans Nesnesi Atıf - Scopus: 2Man-Hour Prediction for Complex Industrial Products(Institute of Electrical and Electronics Engineers Inc., 2023) Unal, Ahmet Emin; Boyar, Halit; Kuleli Pak, Burcu Kuleli; Cem Yildiz, Mehmet; Erten, Ali Erman; Güngör, Vehbi Çağrı; Pak, Burcu Kuleli; Cagri Gungor, VehbiAccurately predicting the cost is crucial for the success of complex industrial projects. There can be several sources contributing to the cost. Traditional methods for cost estimation may not provide the required accuracy and speed to ensure the success of the project. Recently, machine learning techniques have shown promising results in improving cost estimation in various industrial products. This study investigates the performance of gradient-boosting machine learning models and feature engineering techniques on a private dataset of metal sheet project man-hour costs. A comparison of distinct models is conducted, key aspects influencing cost are identified, and the implications of incorporating domain-specific knowledge, including its advantages and disadvantages, are assessed based on performance outcomes. Experimental results demonstrate that LightGBM and XGBoost outperform other models, and feature selection and synthetic data generation techniques improve the performance. Overall, this study highlights the potential of machine learning in metal sheet sampling projects and emphasizes the importance of feature engineering and domain expertise for better model performance. © 2024 Elsevier B.V., All rights reserved.