Browsing by Author "Dağ, Tamer"

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Design and Implementation of a Mobile Prescription System With Patient-Healthcare Professional Interaction
(Kadir Has Üniversitesi, 2015) Öztürk, Çağdaş Egemen; Dağ, Tamer
In this thesis, a mobile prescription reminder and scheduling system application with patient-healthcare professional interaction is designed and implemented. By using the application, different types of users are able to create and manage prescriptions and are reminded to take the medication based on the prescription data. Besides, users are able to reach the prospectuses of the drugs. One of the most important functionality of the application is to provide patient-healthcare professional interaction. By using the application, healthcare professionals can assign prescriptions to their patients and they can also monitor their medicine compliance. Since the medicine compliance is very important to have an effective treatment, the main goal of this application is to help people to take their medicines on time and find the prospectus information of the drugs easily by using their mobile phones.
Developing Novel Techniques for Spatial Domain Lsb İmage Steganography
(Kadir Has Üniversitesi, 2019) Shehzad, Danish; Dağ, Tamer
Steganography is one of the most noteworthy information hiding mechanism, which is used as an alternative to cryptography in order to provide adequate data security. Image steganography is one of the key types of steganography where a message to be transmitted is hidden inside a cover image. The most commonly used techniques for image steganography rely on LSB Steganography. In this thesis, new techniques are developed for LSB image steganography to achieve maximum security, optimal data capacity along with provisioning of efficient steganography mechanism. In the first part of this work, a novel technique based on pairs matching is developed for LSB image steganography. In this technique MSBs along with LSBs are used in a delicate method for data hiding for the first time. The message bits from the secret information are compared with all defined pixel pairs and replace the least two significant bits with respective matched pair number. This technique shows good quality of stego image along with adequate peak signal to noise ratio and provides high payload of secret message. In the second part, threshold-based LSB image steganography technique is developed. This technique also works in spatial domain and categorizes the pixels based on threshold defined categories. Maximum four bits and minimum one bit is embedded in pixel based on its category. The prominence in THBS is on security and payload as it uses bits proficiently for data embedding. ETHBS allows efficient execution of the algorithm along with provisioning of optimal security. In the last part 1LSB Image steganography technique based on blocks matrix determinant is developed. It is a technique in which data is embedded by making minimal changes in image pixels. This technique is 1LSB substitution technique that works on matrix determinant of 2 by 2 blocks of image pixels. This technique ensures high PSNR and ensures good quality of stego image.
Citation - Scopus: 1
Dynamic Multi Threshold Priority Packet Scheduling Algorithms
(EDP Sciences, 2015) Dağ, Tamer; Uzungenç, Sezer
Packet scheduling algorithms are developed in order to use shared transmission resources efficiently. Various application packets such as real and non-real time packets might have different QoS requirements and traditional scheduling algorithms might be insufficient to respond to the applications needs. In this paper two packet scheduling algorithms are proposed to overcome this problem: Dynamic multi threshold priority packet scheduling (DMTPS) and dynamic multi threshold priority with urgency packet scheduling (DMTPUS). The proposed algorithms aim to provide a better QoS level with a decrease in delay time and loss ratio for the low priority packets while still maintaining acceptable fairness towards high priority packets. To evaluate the performance of DMTPS and DMTPUS algorithms they are compared with the commonly used scheduling algorithms such as first come first served (FCFS) and fixed priority. Simulation results illustrate that the dynamic multi threshold priority packet scheduling algorithms can provide a better QoS for low priority packets without decreasing the QoS levels of high priority packets.
Dynamic Multi Threshold Priority Packet Scheduling Algorithms for Wireless Sensor Networks
(Kadir Has Üniversitesi, 2015) Uzungenç, Sezer; Dağ, Tamer
Kablosuz sensör ağlarında farklı türlerde özellikle gerçek zamanlı ve gerçek olmayan zamanlı paket zamanlama gereklidir. Sensörlerin enerji kullanımlarını ve iletim gecikmelerini azaltmak önemlidir. Tezimde yeni paket zamanlama algoritmalarını geliştirerek bunu kablosuz sensör ağlarına entegre etmeye çalışarak enerji kullanımını ve iletim gecikmelerini geliştirerek daha verimli yapıyorum. Tasarladığım dinamik çoklu eşik ve öncelikli paket zamanlama algoritmaları, düşük öncelikli veriler için gecikme zamanını ve veri kaybını azaltarak bunu yüksek öncelikli verilere adil bir şekilde davranarak yapıyor. Eşik algoritmaları günümüzde en çok kullanılan paket zamanlama algoritmalarıyla kıyaslanıyor. Bunlar ilk gelen ilk servis edilir algoritması ile öncelikli paket zamanlama algoritmasıdır. Simülasyon sonuçları gösteriyor ki dinamik çoklu eşik ve öncelikli paket zamanlama algoritmaları düşük öncelikli verilerin servis kalitesini arttırıyor ve bunu yüksek öncelikli verilerin servis kalitesini koruyarak yapıyor.
Dynamic Priority Packet Scheduler With Deadline Considerations (dpd)
(INT INST Informatics & Systemics, 2010) Dağ, Tamer
Providing quality of service (QoS) to applications with different traffic characteristics based on their needs is an important research area for today's and tomorrow's high speed networks. Various techniques have been proposed to achieve good QoS for diverse application types. Among these techniques packet scheduling algorithms decide on how to process packets at network nodes
E-ticaret Pazar Yerleri için Satın Alma Tahmini Üzerine Makine Öğrenmesi Uygulamaları
(2025) Tokuç, Ayşe Aylin; Dağ, Tamer
Bu araştırma, bir e-ticaret kullanıcısının oturum sonunda satın alma yapıp yapmayacağını tıklama akışı verilerini kullanarak tahmin edebilen bir makine öğrenimi çerçevesi önermektedir. Çalışma, son kullanıcı eylemlerinin düzleştirilmiş dizileri, oturum bazlı istatistikler ve her ikisini entegre eden yenilikçi bir hibrit model dahil olmak üzere çeşitli veri temsillerini incelemektedir. Mevcut literatür genellikle tek bir veri temsilini ele alırken, bu araştırma oturum bazlı veriler ile kullanıcı eylemlerinin potansiyel sinerjisini kapsamlı bir şekilde değerlendirmektedir. Önerilen metodoloji, LightGBM'i temel tahmin modeli olarak kullanmaktadır. Ayrıca, karar ağaçları, gradyan artırma, rastgele ormanlar ve lojistik regresyon gibi algoritmalar doğrulama amacıyla uygulanmıştır. Öznitelik önem analizi, satın alma olasılığının temel belirleyicileri olarak son kullanıcı eyleminden bu yana geçen süre, oturum süresi ve belirli ürün etkileşimlerini öne çıkarmaktadır. Bu çalışma, ağaç tabanlı bir tahmin modeli içinde hibrit veri temsillerinin pratik faydasını göstererek, gerçek zamanlı satın alma tahmini için ölçeklenebilir ve yorumlanabilir bir çerçeve sunmaktadır. Bulgularımız, e-ticaret platformlarının satın alma tahminlerini iyileştirmesine ve pazarlama stratejilerini optimize etmesine yönelik uygulanabilir içgörüler sağlamaktadır.
Citation - WoS: 64
Citation - Scopus: 89
Energy Aware Multi-Hop Routing Protocol for Wsns
(IEEE, 2018) Cengiz, Korhan; Dağ, Tamer
In this paper we propose an energy-efficient multi-hop routing protocol for wireless sensor networks (WSNs). The nature of sensor nodes with limited batteries and inefficient protocols are the key limiting factors of the sensor network lifetime. We aim to provide for a green routing protocol that can be implemented in a wireless sensor network. Our proposed protocol's most significant achievement is the reduction of the excessive overhead typically seen in most of the routing protocols by employing fixed clustering and reducing the number of cluster head changes. The performance analysis indicates that overhead reduction significantly improves the lifetime as energy consumption in the sensor nodes can be reduced through an energy-efficient protocol. In addition the implementation of the relay nodes allows the transmission of collected cluster data through inter cluster transmissions. As a result the scalability of a wireless sensor network can be increased. The usage of relay nodes also has a positive impact on the energy dissipation in the network.
An Energy Efficient Routing Algorithm (x-Centric Routing) for Sensor Networks
(INT INST Informatics & Systemics, 2011) Ataç, Göktuğ; Dağ, Tamer
Recent developments in wireless communications and electronics technologies have enabled the progress in low cost sensor networks. Sensor networks differ from traditional networks in several ways such as the severe energy constraints redundant low-rate date and many-to-one flows that the sensor networks require. One of the major challenges facing the design of a routing protocol for Wireless Sensor Networks (WSNs) is to find the most reliable path between the sources and the sink node by considering the energy awareness as an essential design parameter. This paper introduces a new routing protocol called as X-Centric routing by considering the above parameters. Under the X-Centric routing the decision making mechanism depends on the capacity of the sink node by switching between address-centric routing (AC-Routing) and data-centric routing (DC-Routing). The design tradeoffs between energy and communication overhead savings in these routing algorithms have been considered by considering the advantages and performance issues of each routing algorithm.
Citation - Scopus: 1
Extending the Lifetime of Wsns With Maximum Energy Selection Algorithm (mesa)
(Institute of Electrical and Electronics Engineers Inc., 2017) Cengiz, Korhan; Dağ, Tamer
The limited battery supply of a sensor node is one of the most important factors that limit the lifetime of the WSNs. As a consequence increasing the lifetime of WSNs through energy efficient mechanisms has become a challenging research area. Previous studies have shown that instead of implementing direct transmission or multi-hop routing clustering can significantly improve the total energy dissipation and lifetime of a WSN. The traditional LEACH and LEACH based algorithms have evolved from this idea. In this paper we propose a fixed clustering routing algorithm for WSNs which selects the node with maximum residual energy for the following rounds according to a threshold level. The Maximum Energy Selection Algorithm (MESA) can improve the lifetime of the network and reduce the energy dissipation significantly. Our studies have shown that when compared with LEACH and LEACH based algorithms such as ModLEACH and DEEC MESA gains for the lifetime extension and energy dissipation is very important. © 2016 IEEE.
Identification of Critical Proteins Associated With Learning Process for Down Syndrome
(Kadir Has Üniversitesi, 2020) Kulan, Handan; Dağ, Tamer
DS protein profilleri laboratuvarda biyokimyasal teknikler uygulayarak gözlemlenmektedir. Fakat, elde edilen protein listesi uzundur ve listedeki her protein DS ile alakalı değildir. Bu yüzden, DS analizi ve tedavisinde, protein ifade miktarları istatiksel metodlar ve makine öğrenmesi teknikleri uygulayarak analiz edilmektedir. Bu tezde, önceki çalışmalara kıyasla, farklı öndeğerlendirme adımları, özellik seçimi ve sınıﬂandırma teknikleri, farklı veri setleri için protein altkümeleri belirlenmesi için uygulanmıştır. Bu protein altkümeleri fareleri daha doğru şekilde ayrıştırır. Spesifik DS özelliklerinin kritik yolaklara etki eden bu altkümelerdeki proteinler tek tek analiz edildiğinde, seçilmiş proteinlerin öğrenme ve hafıza, sinyal yolakları, Alzheimer hastalığı, bağışıklık sistemi ve hücre ölümü gibi önemli süreçlerde rol aldığı gözlemlenmiştir. Bu tezde seçilen protein alt kümelerinden DS un farklı semptomlarını anlamak için yararlanılabilinir ve DS tedavisinde etkili ilaçlar geliştirmek için kullanılabilinir. The protein proﬁles of people with DS are observed by applying biochemical tech niques in laboratory. However, the list of analyzed proteins is long and not all proteins in list are not related to DS. Thus, for the analysis and the treatment of DS, protein expression levels have been analyzed by applying statistical procedures and machine learning techniques. In this thesis, compared to previous works, different preprocessing steps, feature selection and classiﬁcation techniques are applied to deﬁne the subsets of proteins for datasets. These subsets diﬀerentiate mice more accurately. When these subsets which aﬀect the critical pathways of speciﬁc DS aspects are analyzed, it is monitored that selected proteins have vital roles in the processes, such as apoptosis, learning and memory, signaling pathways, immune sys tem and Alzheimers disease (AD). The subsets of proteins selected in this thesis can be applied to interpret the causes of diﬀerent symptoms in DS and can be utilized to foster eﬀective drugs for the cure of DS.
Implementation of a Sdn (software Defined Network)
(International Institute of Informatics and Systemics IIIS, 2014) Ergen, Mithat Sinan; Kutlan, Sarp; Dağ, Tamer; Dağ, Hasan
Improving resource efficiency enhancing network security and achieving simpler network management have become the main goals for networking in the previous years. To accomplish these ideas an efficient routing traffic monitoring access control and server load balancing systems need to be designed. However these objectives make the optimization and management of networks rather difficult. In this paper Software Defined Networks (SDN) an alternative way for creating an optimized network by taking into account the difficulties met today is introduced. Software Defined Networks provide the separation of control and data planes for the switches which allows programming for a customized control plane. With simplified network management through SDN it can be possible to dynamically adjust the behavior of the network equipment independently from equipment manufacturers. New mechanisms with new potential benefits can easily be explored and used. Quality of Service and network security problems can be solved rapidly. In this paper it is described how to build a virtualized SDN in order to show the benefits of SDN over a distributed network. Virtualization can provide deployment and delivery flexibility cost savings and improved user experience.
Importance of Regional Differences in Brain Throughout Aging for Down Syndrome
(Association for Computing Machinery, 2018) Kulan, Handan; Dağ, Tamer
Down syndrome (DS) which affects approximately one in 700 live births is caused by an extra copy of the long arm of human chromosome 21 (HSA21). Statistical analysis has been done for understanding the protein expression profiles based on age and sex differences in DS. In addition there are ongoing research efforts for comprehending expression patterns based on different brain regions. However little is known about the mechanisms of expression differences in brain regions throughout aging. Insights into these mechanisms are required to understand the susceptibility of distinct brain regions to neuronal insults with aging. Dissection of this selective vulnerability will be critical to our understanding of DS. By extracting information from the critical proteins which take part in the mechanism of the molecular pathways the diagnosis of DS can become easier. Also understanding the molecular pathways can contribute to develop effective drugs for the treatment of DS. In this work forward feature selection technique is applied for determining the protein subsets for old and young mice datasets which consist of the expression profiles across different brain regions. When these subsets are analyzed it is observed that selected proteins play important roles in the processes such as mTOR signaling pathway AD MAPK signaling pathway and apoptosis. We believe that the subsets of protein selected in our work can be utilized to understand the process of DS and can be used to develop age-related effective drugs.
Citation - WoS: 13
Citation - Scopus: 21
Improving Energy-Efficiency of Wsns Through Lefca
(Sage Publications Inc, 2016) Cengiz, Korhan; Dağ, Tamer
Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence the life span and the energy consumption of a WSN have become a challenging research area. According to the existing studies instead of using direct transmission or multihop routing clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. In this paper we propose a low energy fixed clustering algorithm (LEFCA) for WSNs. With LEFCA the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network but also decreases the energy dissipation significantly.
Citation - WoS: 3
Citation - Scopus: 5
In Silico Identification of Critical Proteins Associated With Learning Process and Immune System for Down Syndrome
(Public Library Science, 2019) Kulan, Handan; Dağ, Tamer
Understanding expression levels of proteins and their interactions is a key factor to diagnose and explain the Down syndrome which can be considered as the most prevalent reason of intellectual disability in human beings. In the previous studies the expression levels of 77 proteins obtained from normal genotype control mice and from trisomic Ts65Dn mice have been analyzed after training in contextual fear conditioning with and without injection of the memantine drug using statistical methods and machine learning techniques. Recent studies have also pointed out that there may be a linkage between the Down syndrome and the immune system. Thus the research presented in this paper aim at in silico identification of proteins which are significant to the learning process and the immune system and to derive the most accurate model for classification of mice. In this paper the features are selected by implementing forward feature selection method after preprocessing step of the dataset. Later deep neural network gradient boosting tree support vector machine and random forest classification methods are implemented to identify the accuracy. It is observed that the selected feature subsets not only yield higher accuracy classification results but also are composed of protein responses which are important for the learning and memory process and the immune system.
Indoor positioning system development
(Kadir Has Üniversitesi, 2015) Alp, Ebru; Dağ, Tamer; Arsan, Taner
Nowadays, smartphone market penetration continues to grow with developing technology. Accordingly, position detection in closed areas has become an important research area. For instance; finding a direct route to the gate based on location at an airport, determining a route to the destination that could be a shop or cafe at a shopping center or informing about sales discount to increase sales using location are several applicable areas of position estimation. In the thesis, I developed triangulation algorithm more efficient using least square method with the developments of Wi-Fi channel fixing, optimized A and n values used in log normal formula and more than 3 access points. I used synthetic data which is created from sample data and estimate location for comparison to analyzing success rate of algorithm. According to the measurement results, triangulation algorithm with least square method, channel fixing, optimized A and n values, more than 3 Access Points gives accurate location in closed areas more than simple triangulation algorithm does. The thesis will lead to detect position in closed areas and use it in daily lives using triangulation algorithm with least square method.
Investigation the Risk of Autism by Evaluating Prenatal and Postnatal Exposure To Traffic-Related Air Pollution
(Kadir Has Üniversitesi, 2020) Demir, Tamer; Dağ, Tamer
Autism spectrum disorder (ASD ) which is a group of neurodevelopmental disorder that appears during the first few years of a child's life affecting a child's communication and socialization abilities with increasing prevalence. Recently, several recent studies have found associations between exposure to traffic-related air pollution (TRAP) and ASD. The primary aim of this study is to investigate/examine the relation between TRAP and four air pollutants (NO2, O3, PM10, PM2.5) and ASD during prenatal or post-natal by using multiple logistic regression models and variable selection methods. Results show that the adjusted odds ratio (AOR) for ASD per IQR increase was strongly associated for exposure to NO2 during the first year period, was moderately associated for exposure to NO2 (from interstate highways during the third trimester; from the county highway during the first year; from city street during the first year; from all roads during the all pregnancy; from all roads during the first trimester) and O3 during the second year, and weakly associated with exposure to NO2 from interstate highways during the second trimester, O3 during the first trimester and PM2.5 during the second year. Additionally, comparing fourth to first quartile exposures the AOR was 15.47 for NO2 from interstate highways during the third trimester, was 5.00 for NO2 from all roads during the first trimester, and comparing third to first quartile exposures the AOR was 2.31 for PM2.5 during the second year. As a result, a strong relationship between NO2 exposure and ASD was detected for each 7.1 ppb [IQR] increase in NO2 during the first year and subjects exposed to a higher level of NO2 during the first and third trimester, and PM2.5 during the second year was also associated with increased risk of ASD.
Citation - WoS: 13
Citation - Scopus: 17
Low Energy Fixed Clustering Algorithm (lefca) for Wireless Sensor Networks
(IEEE, 2015) Cengiz, Korhan; Dağ, Tamer
Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence the life span and the energy consumption of a WSN has become a challenging research area. According to the existing studies instead of using direct transmission or multi-hop routing clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. In this paper we propose a low energy fixed clustering algorithm (LEFCA) for WSNs. With LEFCA the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network but also decreases the energy dissipation and increases the throughput significantly.
Low Energy Fixed Clustering Algorithm for Wireless Sensor Networks
(Kadir Has Üniversitesi, 2016) Cengiz, Korhan; Dağ, Tamer
Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence the life span and the energy consumption of a WSN has become a challenging research area. According to the existing studies instead of using direct transmission or multi-hop routing clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. in this thesis low energy fixed clustering algorithm (LEFCA) and multihop low energy fixed clustering algorithm (M-LEFCA) are proposed for WSNs. With LEFCA the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network but also decreases the energy dissipation significantly. in addition proposed M-LEFCA uses multi-hop intra cluster communication approach. it selects optimum forward neighbor cluster heads (CHs) as relay nodes (RNs). M-LEFCA aims to reduce energy dissipation and prolong network lifetime of LEFCA by combining clustering and multi-hop routing approaches.
Citation - WoS: 11
Citation - Scopus: 13
Lsb Image Steganography Based on Blocks Matrix Determinant Method
(KSII-KOR SOC Internet Information, 2019) Shehzad, Danish; Dağ, Tamer
Image steganography is one of the key types of steganography where a message to be sent is hidden inside the cover image. The most commonly used techniques for image steganography rely on LSB steganography. In this paper, a novel image steganography technique based on blocks matrix determinant method is proposed. Under this method, a cover image is divided into blocks of size 2 x 2 pixels and the determinant of each block is calculated. The comparison of the determinant values and corresponding data bits yields a delicate way for the embedment of data bits. The main aim of the proposed technique is to ensure concealment of secret data inside an image without affecting the cover image quality. When the proposed steganography method is compared with other existing LSB steganography methods, it is observed that it not only provides higher PSNR, lower MSE but also guarantees better quality of the stego image.
Max-Pivot Routing for Opportunistic Networks
(2013) Dağ, Tamer
Opportunistic networks are challenging types of networks where network connections are imminent. Network topologies are dynamic and can rapidly change. A path between a source node and a destination node may or may not exist the network can be disconnected. This type of behavior observed under opportunistic networks makes classical networking solutions impractical. Thus traditional routing algorithms are not suitable for such networks and will not be useful. Although flooding might be seen as the best solution to reach a destination under opportunistic networks flooding solutions' extensive usage of network resources is an extreme overhead. In this paper max- pivot routing for opportunistic networks is proposed and described. With max-pivot routing it is observed that the induced network traffic is significantly reduced while still achieving the benefits of a flooding based routing. The performance comparisons of max-pivot routing and flooding based routing methods show that max-pivot routing can be a successful routing method for opportunistic networks.