Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı Tez Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/418

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Accelerating computer algorithm by using GPU
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yalçın, Salih; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Travelling Salesman Problem (TSP) is one of the significant problems in computer science which tries to find the shortest path for a salesman who needs to visit a set of cities and it involves in many computing problems such as networks, genome analysis, logistic etc. Using parallel executing paradigms, especially GPUs, is appealing in order to reduce the problem-solving time of TSP. One of the main issues in GPUs is to have limited GPU memory which would not be enough for the entire data. Therefore, transferring data from host device would reduce the performance in execution time. In this study, we present a methodology for compressing data to represent cities in the TSP so that we include more cities in GPU memory. We implement our methodology in Iterated Local Search (ILS) algorithm with 2-opt and show that our implementation presents 29% performance improvement compared to the state-of-the-art GPU implementation.
Adaptive online torque sharing function to mitigate torque ripple in switched reluctance motors
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Genç, Ufuk; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Electrical machines play a crucial role in modern society by transforming electrical energy into mechanical energy and vice versa. These machines include various types of motors and generators, which are used in a wide range of applications such as electric vehicles, industrial automation, and renewable energy systems. One of the popular electrical machines is the switched reluctance machine (SRM), which is known for its high reliability and efficiency. The key advantages of the SRM include its simple structure, robustness, and low cost. The SRM does not require a permanent magnet or an excitation winding, making it an attractive option for high-volume, low-cost applications. Despite its advantages, the SRM also has some disadvantages that need to be considered. One of the main drawbacks of the SRM is being susceptible to torque ripple, which can result in vibration and noise. In order to overcome these disadvantages, advanced control methods have been developed for the SRM. One such control method is the torque sharing function, which distributes the load among the phases of the motor. This results in improved torque characteristics and reduced torque ripple. However, this control method also has some disadvantages, such as increased complexity and the need for more advanced sensors and controllers. Additionally, the torque sharing function may result in reduced efficiency, especially at high speeds. The purpose of this thesis study is to improve the torque ripple performance of SRM for a wide speed range through the proposed control approach. In conclusion, minimizing the torque ripple is a critical aspect of the operation of SRMs, and a range of control strategies and techniques can be used to achieve this goal. By reducing the torque ripple, SRMs can deliver improved efficiency, performance, and reliability, making them even more attractive for a wide range of applications.
Analysing network traffic and detecting network threats by using the algorithms of machine learning
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Küçükkoç, Abdurrahman; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
As information technologies progress, the possibilities of access to information increase and therefore it becomes difficult to ensure the security of information. Today, with the use of information systems in all areas of life, network threats have also increased. The increase in individual access to and use of the internet has also brought network threats. In addition, the latest developments in information technologies, developing global communication networks, the internet of things aiming to connect all objects with networks, cloud technologies, the spread of mobile internet and the renewal of devices have brought network threats and uncertainties. Network threats increase the security vulnerabilities in the information and communication systems of individuals and organisations day by day. This situation causes systems to malfunction, economic damage and cyber security to be jeopardised. In order to contribute to individuals, institutions and organisations, our thesis aims to protect information systems against network threats, to ensure data confidentiality, integrity and accessibility, to detect network threats in advance and to take measures against these threats. We believe that by analysing heterogeneous network traffic, which includes most network attacks on the Internet, and using machine learning algorithms, we will reach a result close to reality in the detection of network threats. In line with this result, we will be able to take precautions against network threats in information systems and structures
Antimicrobial peptide activity prediction using machine learning methods
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Söylemez, Ümmü Gülsüm; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this thesis, using the multiple properties of the peptides we aimed to develop machine learning approaches that can predict the antimicrobial activities of the peptides. We have created two datasets for the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately. In our first study, ten different physico-chemical properties of the peptides were calculated, and used as features of the peptides. Following the data exploration and data preprocessing steps, a variety of classification models were build with 100-fold Monte Carlo Cross-Validation; and the performance of these models were evaluated. In the second study, we proposed a novel method called AMP-GSM. The method was tested for three datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. In the last study, using motif matching score with the models of activity against Gram-positive and Gram-negative bacteria, we created novel peptides and predicted the target selectivity of these peptides. The studies presented in this thesis advance the field of computational research by making it easier to predict the possible antimicrobial effects of peptides and to design AMPs in wet laboratory environments.
Automated processing and classification of medical thermal images
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Özdil, Ahmet; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
The aim of this dissertation is to develop computer aided methods for processing and evaluating medical infrared thermal images. Throughout this study three problems were evaluated. The first problem was to automatically classify the body part and pose in the thermal images. In this study there were four classes; upper-lower body parts with back-front views. The first step included the segmentation of the background with Otsu’s thresholding method applying histogram equalization. Next, DarkNet-19 architecture was used to extract features from images and these features were reduced using PCA and tSNE methods. Finally reduced feature sets were used for classification. The second problem was to automatically classify liver steatosis from using thermal images. In this study, the classification problem was tested on an anatomical region of interest from abdominal images corresponding to the liver. Deep learning and texture analysis methods were employed for feature extraction, and then the selected feature sets were used for classification. The third problem was to quantify thermograms of multiple sclerosis (MS) patients for better assessment of the disease and monitoring the therapy. Thermal images of two patients and a healthy control from lower limbs were evaluated during experiments, and localized quantification of the effect of MS on the feet of the patients using thermal images method was proposed. The proposed method was fully correlated with the evaluations of physician. It is shown that medical thermal imaging has high potential in many fields of medicine as a non-invasive method for pre-diagnosis and follow-up.
An autonomous heterogeneous multi-robot system design for early fire detection
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Serin, Ömer Faruk; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
The usage of autonomous multi-robot systems for human life-endangering applications is emerging. Early wildfire detection and firefighting are two example applications. In this study, a heterogenous multi-robot system is proposed for both fire detection and response. The system employs an unmanned aerial vehicle for beyond-visual line-of-sight observations and an unmanned ground robot for fire extinguisher carrying. The proposed method uses ultrawideband (UWB) communication and ranging modules for the relative localization of robots during their movements. A specially trained YOLOv7 object detection model is used for robustly detecting forest fires and smoke while a modified version of the Vector Field Histogram Plus (VFH+) algorithm on the ground robot is used for obstacle avoidance while navigating. The structural design of the system requires no odometry or mapping of the environment hence improving the applicability of the system while decreasing system complexity. Additionally, the proposed UWB localization system is shown to be robust in long-lasting operations unlike many odometry-based approaches which accumulate errors with time. Moreover, localization of the UAV is realized with only three independent UWB-based range measurements and the altitude information of the UAV. The system is tested both in a realistic simulation environment and in real experimental setups with multiple runs. Results showed that the proposed system is improvable for better detection and practical to implement even in a dense forest environment without the need for GPS sensors, odometer data, or magnetometer.
BLOCKCHAIN BASED DATA SHARING PLATFORM FOR BIOINFORMATICS FIELD
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2020) ADANUR, Beyhan; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Recently, panomics studies attempt to identify new and actionable biomarkers by combining -omics data with other data types. In this context, there is a need to develop secure platforms that take into account ethical aspects and solve privacy and ownership issues as well as data sharing for an accurate analysis of -omics data. These days, blockchain technology has picked up significant attention in genomics, since it offers a new solution to these problems from a different perspective. In this thesis, we proposed a hybrid platform called GenShare, which is based on blockchain, homomorphic encryption and intel software guard extension (SGX) to provide efficient genomic data sharing, to perform statistical analysis and other similar processes on genomic data. While the proposed model solves security-privacy issues using homomorphic encryption and SGX, it solves other issues by using a combination of Hyperledger Fabric and Ethereum networks. In this study, Hyperledger Fabric network, which is the first phase of the GenShare model, setup is made and the performance of the network is tested with a different number of workloads. At the end of our performance evaluations, we concluded that the GenShare model has a potential to speed up the process of collecting and sharing data and it offers an efficient platform for the participants.
Blockchain based peer-to-peer energy trading applications
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Seven, Serkan; 0000-0003-2611-720X; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
This thesis explores the potential of innovative peer-to-peer (P2P) energy trading schemes for virtual power plants (VPPs) using blockchain technologies, smart contracts, and decentralized finance (DeFi) instruments. Traditional centralized approaches have limitations in terms of transparency and security, which can hinder the successful implementation and operation of VPPs and P2P energy trading systems. The dissertation begins by reviewing the current state of energy sources within the global energy landscape. Understanding the existing landscape provides valuable insights into the potential benefits and challenges of implementing P2P energy trading within VPPs. The focus of the dissertation is to develop and analyze innovative P2P energy trading schemes for VPPs that integrate blockchain technologies and facilities to enhance transparency, security, and automation of energy transactions. Furthermore, DeFi instruments, specifically decentralized exchange (DEX), are used as a novel approach instead of auction methods to determine P2P energy buying and selling prices. Along with blockchain technologies, optimization is used to maximize the economic benefits of peers. The sequential decision problem of the trading schemes is solved with mixed integer linear programming (MILP). In addition, machine/deep learning models are utilized to overcome the drawbacks of conventional mathematical programming like MILP. These models can accelerate the decision-making processes by learning from the optimization results obtained. Overall, frameworks for the successful integration of P2P energy trading within and among VPPs are developed to validate the effectiveness and feasibility of the proposed P2P energy trading schemes through case studies and simulations using realistic data sets and blockchain platforms.
Blockchain-based energy applications: DSO perspective
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Yağmur, Ahmet; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
This thesis discusses blockchain-based energy applications from the distribution system operator (DSO) perspective. Blockchain has a potential impact on emerging actors, such as electric vehicles (EVs), charging facility units (CFUs), Distributed Energy Resources (DERs) and microgrids of the electricity grid. Although, blockchain offers magnificent, decentralized solutions, owing to the reality of the existing grid structure, the central management of DSOs still plays a significant, non-negligible role. Numerous studies of proposed blockchain-based EV systems have investigated the energy costs of EVs, fast and efficient charging, privacy and security, peer-to-peer energy trading, sharing economy, selection of appropriate location for CFUs, and scheduling. Additionally, blockchain in DERs, microgrids and energy market investigated in literature. However, cooperation with DSO organizations has not been adequately addressed. Blockchain-based solutions mainly suggest an entirely distributed and decentralized approach for energy trading. However, converting the entire power system infrastructure is considerably expensive. Building a thoroughly decentralized electricity network is nearly impossible in a short time, particularly at the national grid level. In this regard, the applicability of the solutions is as significant as their appropriateness, especially from the DSO perspective, and must be examined closely. The blockchain applicability of the essential DSO services such as SCADA and AMI are analyzed in this study. Time series analysis applied to forecast future peak load of the grid in a pilot region. Reducing the peak load by using BC based demand side management mechanism scenario evaluated and total saving of grid investment is analyzed. We searched and analyzed DSO-based requirements for potential blockchain applications in the energy sector.
CAMERA BASED SHEET MEASUREMENT SYSTEM FOR LASER CNC MACHINES
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2018) UMAR, Aamish; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Laser CNC machines are widely utilized for cutting metal sheets of varying thickness and materials. The sheets to be cut can be of varying dimensions and be placed at any desired area of the cutting table. The operator needs to assign the starting point of the laser manually along with the dimensions of the metal sheet in order to start the cutting process. The process of assigning the starting point and dimension of sheet are time consuming and can take few minutes before every cutting process and it sums up to hours by the end of daily cutting jobs, an automated process of sheet measurement can save considerable amount of time and speed up the process. In this thesis, a camera based system for automatic sheet measurement which includes measurement of starting point assignment, orientation, length and breadth has been developed. The algorithms have been implemented keeping in mind the importance of speed since the processing has to be done in real time and needs to be as fast as possible. The implemented algorithms can find all required parameters in about two seconds. The techniques utilized for its implementation have been discussed. The robustness of the system has been compared with other traditional methods of sheet measurement and orientation detection. The implemented system was tested on a real laser CNC machine over a period of six months and the test results have been discussed. Also, a camera based intrusion detection system for laser CNC machine has been developed in order to make it safe for human during operation. Patent application made for the implemented system.
Classification of microRNA-disease associations and microRNA-species associations based on k-mer sequence representation
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Erbaşı, Yalçın Han; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
The dysregulated gene expression brings about a variety of diseases, and dysregulation of microRNA (miRNA) has a wide impact on disease development and cellular physiology. Thus, miRNAs play important roles in a variety of fundamental and significant biological processes related to human diseases. There are a lot of research about changes in the function of miRNAs have been published in many human diseases. Computational methods serve as a complementary process to traditional wet-lab experiments, which require many resources and time in terms of detecting potential miRNA-Disease associations. Furthermore, there is a need to present a novel approach that allows assignment of an unknown miRNA to its most likely species. An easy way to filter new data would be to ensure that the new miRNA is classified below the maximum distance to the species known to originate from. In this thesis, a computational model has been proposed for identifying miRNA-disease and miRNA-Species associations by depicting the miRNAs with their k-mer sequence representation and by utilizing machine learning methodologies. The difference of our approach is which we reveal disease and species associated the sequences of miRNA store information. This put a question about the miRNA's chemical compounds and their associations with different types of species and diseases. With this study, the new disease-disease and species-Species associations disclosed can be calculated for many different species and diseases, these approaches can develop to species and disease classification. Lastly, our study may open a door to redefine species and diseases classifications which have been used nowadays, also it may provide the improvement of treatment strategies and early diagnosis
Cloud induced PV impact on voltage profiles for smart microgrids
(Abdullah Gül Üniversitesi, 2018) ÇAĞATAY KOÇER, MUSTAFA; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü; ÇAĞATAY KOÇER, MUSTAFA
In the history of humanity, no other invention has positively influenced everyone's life as much as the invention of electrical energy. With the electricity, the rise of civilization gained momentum, industrial technologies advanced, and scientific developments found more suitable habitat for themselves. However, in order to meet the growing demand for electricity, production costs had to be reduced. In this direction, the energy sector used fossil fuel-based solutions for cheap electricity production. However, nowadays, a tendency to use cleaner and more sustainable methods for electricity production has occurred since fossil fuel sources are limited and they increase the greenhouse gas emissions in the atmosphere. This trend brings renewable energy resources (RER) to the table as a new solution, especially in the modern electricity networks. However, since behaviors of the RERs are challenging to forecast and highly dependent on environmental factors, these resources have some severe problems in the integration into the grid, particularly in the low voltage networks, such as microgrids. In this thesis, the impact of the fluctuations in photovoltaic power (PV) generation, which happens because of frequently interrupted solar radiance by the chaotic movements of the clouds, on the load voltage levels of a real field microgrid system belonging to the Malta College of Arts Science and Technology (MCAST) campus is investigated. Also, the impact of the auxiliary sources (battery storage system and diesel generator) that are responsible for ensuring that the microgrid healthily continues its operation on the load voltage profiles is presented. The author used the MATLAB/Simulink platform for the necessary simulations and system designs
COLLOIDAL PEROVSKITE NANOCRYSTALS AND LED APPLICATIONS
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2019) BEŞKAZAK, Emre; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Colloidal perovskite nanocrystals, recently, attracted a great amount of research interest because of their near-unity photoluminescence quantum yield efficiency, narrow emission linewidth, easy tunability of wavelength covering full visible spectrum thanks to quantum confinement effect and halogen composition dependence, as well as the compatibility with the flexible substrates. All these properties make colloidal perovskite nanocrystals a serious candidate for use as emissive layers in next-generation light-emitting diodes (LEDs) and displays. In this thesis, syntheses of colloidal nanocrystals were demonstrated, and red and green light-emitting diodes which contain perovskite nanocrystals as emissive layer were fabricated, optimized, and measured. The main challenges of perovskite nanocrystals based light-emitting diode applications are identified, such as stability and toxicity. Additionally, blue and green light-emitting diodes which employ CdSe based semiconductor nanocrystals as emissive layer were fabricated on ITO coated PET (Polyethylene terephthalate) and glass substrates. A flexible white LED is presented as a proof of concept, by fixing a flexible polymer layer consists of red and green InP based semiconductor nanocrystals on top of the CdSe based blue LED.
Computer aided detection of cancer using histopathology images
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yengeç-Taşdemir, Sena Büşra; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Detecting colon adenomatous polyps early is crucial for reducing colon cancer risk. This thesis investigated various deep learning approaches for computer-aided diagnosis of colon polyps on histopathology images using deep learning. The thesis addressed key challenges in polyp classification, including differentiating adenomatous polyps from non-adenomatous tissues and multi-class classification of polyp types. Initially, a histopathology image dataset is collected and refined from Kayseri City Hospital. The first study used stain normalization algorithms and an ensemble framework for binary classification, achieving 95% accuracy on the custom dataset and 91.1% and 90% on UnitoPatho and EBHI datasets, respectively. The second study implemented a tailored version of the supervised contrastive learning model for multi-class classification, outperforming state-of-the-art deep learning models with accuracies of 87.1% on custom dataset and 70.3% on UnitoPatho dataset. The third study proposed a self-supervised contrastive learning approach for utilizing all data in cases of limited labeled images. This approach achieved better performance than transfer learning with ImageNet pre-trained models. In conclusion, this PhD thesis investigated deep learning approaches for computer-aided diagnosis of colon polyps on histopathology images, demonstrating high accuracy in binary and multi-class classification, outperforming state-of-the-art models. These findings contribute to improving colon polyp classification accuracy and efficiency, ultimately facilitating the early detection and prevention of colon cancer.
Control algorithms for feedback tracking in the small populations of Hodgkin-Huxley neurons
(Abdullah Gül Üniversitesi, 2018) ŞENEL, ZEYNEP; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü; ŞENEL, ZEYNEP
The purpose of the thesis is to design powerful mathematical control algorithms for the tracking and modeling spiking and bursting behaviors of real biological neurons in 4-dimensional dynamical systems. For this aim, 4-dimensional Hodgkin-Huxley’s (HH) nonlinear dynamical system including differential equations preferred. Because HH model represents a realistic mathematical model for the real neurons and it analytically accepted. Applied external current as a control signal initiate stimulating of the neuron cells in the neuronal networks serve while the membrane action potentials are outputs. We applied two different control methods; speed gradient (SG) of Fradkov’s and target attractor (TA) of Kolesnikov’s feedbacks for the modeling and controlling spiking and bursting regime that axon membrane potential created by the control signal in HH neuron clusters. These algorithms show high effectiveness and robustness in the managed HH dynamical neuron system. This study provides generating arbitrary forms of single spikes, train of spikes and bursts for chosen cells in the various configurations of HH neuron clusters (linear chain and ring-type chain) with the control over a selected element of the network. In this study, developed algorithms applied to epileptiform collective bursting in a small cluster of HH neurons for make suppression. The scope of this thesis is to develop new control methods for mathematical modeling to control of real neurons and effectively can use in computational neuroscience and diagnosis or treatment of neural dysfunctions such as epileptiform or abnormal behavior in the HH neuron networks.
Coordinated target detection and tracking by drones using distance and vision
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Alabay, Hüsnü Halid; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Robot autonomy refers to the ability to carry out objectives by perceiving the environment and deciding on the actions required without human interruption. Although autonomous aerial robots offer big advantages in our daily life, online localization and control remain the biggest challenge lying ahead of aerial robot implementations. For single robot applications, GPS, and motion capture (mocap) systems can be utilized for outdoor and indoor applications, respectively. However, when it comes to multi-robot systems, the relative localization problem needs to be solved beyond the single robot localization problem. Furthermore, GPS signals are not available everywhere, and mocap systems limit the application space of multi-robot systems. Motivated by the industrial application scenarios, we address the relative localization and docking problem in multi-drone systems where drones do not utilize any external infrastructure for localization. We consider a two-drone system that aims at docking a target object which consists of an ultrawideband (UWB) distance sensor. The drones are equipped with UWB sensors and cameras and try to localize the target object and dock around it in a pre-defined configuration in the absence of GPS and magnetometer sensors and external infrastructures. We design an extended Kalman filter based on the dynamic model of the drone-target configuration that fuses the distance and vision sensor outputs. Particularly, we use the YOLO algorithm for the bearing detection between the drones and the target. Next, we devise and implement a switching-based distributed formation control algorithm and integrate it into the estimation algorithm. We demonstrate the performance of our algorithm in several simulation studies in a realistic Gazebo environment. Finally, we provide primary experimental results and a roadmap to the full implementation of the system.
Decentralized electronic health record management system and disease prediction with machine learning methods
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Dedetürk, Beyhan Adanur; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Electronic health records (EHRs) are vital to the advancement of healthcare and can help detect and prevent diseases early. However, EHR sharing faces challenges such as managing large data volumes, ensuring data privacy, security, and interoperability. This thesis aims to develop and analyze a blockchain-based EHR sharing system for disease prediction mechanism integration using SysML. The AguHyper platform, built by merging the InterPlanetary File System (IPFS) with Hyperledger Fabric, ensures the immutability of health records by storing hash values in the blockchain and encrypted records in IPFS. The system architecture and implementation configurations, including CouchDB and the Raft consensus mechanism, are thoroughly examined. The study also presents a novel hybrid approach called CSA-DE-LR, which integrates Differential Evolution (DE) and Clonal Selection Algorithm (CSA) with Logistic Regression (LR) to improve LR weights for precise categorization of cardiovascular diseases. The integration of the AguHyper with the CSA-DE-LR is explained in detail. At the end of our performance evaluations, we concluded that the AguHyper model has the potential to speed up the process of collecting and sharing data, and it offers an efficient platform for the participants.
Deep learning models for traffic volume prediction
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Çini, Nevin; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
In the last 50 years, with the growth of cities and increase in the number of vehicles and mobility, traffic has become troublesome. As a result, traffic flow prediction started to attract attention as an important research area. However, despite the extensive literature, traffic flow prediction still remains as an open research problem, specifically for long- term traffic flow prediction. Compared to the models developed for short-term traffic flow prediction, the number of models developed for long-term traffic flow prediction is very few. Based on this shortcoming, in this study, we focus on long-term traffic flow prediction and propose a novel deep ensemble model (DEM). In order to build this ensemble model, first, we developed a convolutional neural network (CNN), a long short term memory (LSTM) network, and a gated recurrent unit (GRU) network as deep learning models, which formed the base learners. In the next step, we combine the output of these models according to their individual forecasting success. We use another deep learning model to determine the success of the individual models. Our proposed model is a flexible ensemble prediction model that can be updated based on traffic data. To evaluate the performance of the proposed model, we use a publicly available dataset. Numerical results show that our proposed model performs better than individual deep learning models (i.e., LSTM, CNN, GRU), selected traditional machine learning models (i.e., linear regression (LR), decision tree regression (DTR), k-nearest-neighbors regression (KNNR) and other ensemble models such as random-forest-regression(RFR).
Design and development of machine learning models for disease prediction and biomarkers detection
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Temiz, Mustafa; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
In medical science, the prediction of diseases and the identification of biomarkers play an important role in the diagnosis and treatment of various health conditions. The recent proliferation of data mining techniques has accelerated the development of disease prediction systems. In particular, machine learning methods are an effective way to analyze medical data and identify patterns to predict the likelihood of the disease development. Machine learning methods also help to identify biomarkers. Recently, the increasing incidence and mortality rates of inflammatory bowel disease, colorectal cancer and type 2 diabetes have drawn researchers' attention to these research areas. The aim of this thesis is to reduce the number of features and improve the prediction performance of machine learning based on complex biological datasets with a large number of disease-related features, as well as to identify potential biomarkers. In this thesis, three different studies are presented. The first study predicts eleven different cancer subgroups using miRNA data and biological domain knowledge and identifies potential biomarkers for these diseases. The second study predicts three different diseases using metagenomic data and biological domain knowledge and identifies potential biomarkers. The third study uses metagenomic data related to colorectal cancer to conduct global and population-based comprehensive experiments with traditional feature selection methods to identify potential biomarkers. This thesis presents a promising avenue for early disease detection, facilitating expedited treatment protocols, improving human survival rates, and potentially alleviating economic burdens within these critical research domains.
Design and implementation of nanophotonic architectures using smart-self assembly of colloidal nanomaterials
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Şenel, Zeynep; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
DNA-driven self-assembly techniques offer precise control over the positioning of colloidal nanoparticles through specific Watson–Crick interactions, and its reversibility via controlling the temperature of medium. This thesis explores an alternative strategy to control DNA-functionalized nanoparticles' binding/unbinding process by leveraging laser radiation, inducing localized heating within the nanoparticles to facilitate disassociation. First, we demonstrate the active manipulation of the optical properties of DNA-assembled gold nanoparticle networks via external optical excitation. Specifically, irradiation with a green hand-held laser yields a substantial ∼30% increase in total transmittance, accompanied by a transition from opaque to transparent states observable in optical microscopy images. The reversibility of this process is demonstrated by the restoration of the nanoparticle network post-irradiation cessation, underscoring the efficacy of optical excitation in tailoring both the structure and optical characteristics of DNA-mediated nanoparticle assemblies. Second, we introduce a method to tailor DNA-driven self-assembly of semiconductor nanoparticles on glass by applying an external optical field. A green laser directs the assembly of DNA-functionalized red-emitting quantum dots (QDs) on DNA-functionalized glass, leaving uncoated spots owing to localized heating. This effect becomes prominent after three hours of radiation using a laser with an irradiance of 57.1 W/cm2. Experiments with different lasers and nanoparticle types confirm the role of laser-induced heating in preventing QD-glass bonding via DNA-DNA interaction. Secondary coating of previously uncoated spots with DNA-functionalized green-emitting QDs and dye-functionalized DNAs indicates a successful hierarchical self-assembly. Our findings highlight the potential of light-assisted DNA-driven self-assembly for diverse nanoparticle architectures, promising applications in optoelectronics and nanophotonics.

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