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ÖgeDeep wavelet neural network for spatio-temporal data fusion(Graduate School, 2022-07-19)Machine Learning (ML) algorithms have recently gained prominence in prediction problems. The construction of an accurate machine learning model becomes a real challenge concerning the nature of the data, the number of data samples as well as the accuracy and complexity of the model. This study introduced a new machine learning structure for temporal and spatio-temporal, univariate, and multivariate prediction problems. The predictive error compensated neural network model (PECNET), which combines spatio-temporal data, has been developed. Temporal data contains information within the observation time window, and its bandwidth is limited by the sampling rate. On the other hand, spatial data provide information regarding spatial location, while spatio-temporal data combine temporal and spatial resolution together. The PECNET model can capture both time dependencies and the spatial relationships between different data resources by fusing multivariate input patterns at multiple lengths and the sampling resolution. The PECNET achieves reliable prediction performance with relatively low model complexity and minimizes the overfitting and underfitting problems. In the proposed model, additional networks are used to predict the error of previously trained networks to compensate the overall prediction. The main network uses high correlation data with the target through moving frames in multiple scales. The PECNET improves time series prediction accuracy by enhancing orthogonal features within a data fusion scheme. The same structure and hyperparameter sets are applied to quite different problems to verify the proposed model's robustness and accuracy. Root-zone soil moisture, wind speed, financial time series data, and stationary and non-stationary time series benchmark problems are selected to evaluate the PECNET model. The results have shown improvement in the prediction accuracy and overfitting prevention using multiple neural networks for distinctive types of problems. The first part of this dissertation focuses on designing and implementing the proposed PECNET model. The algorithm is implemented in the Python programming language, and the performance of the proposed algorithm is evaluated on stochastic and chaotic time series benchmark problems found in the literature. Results have highlighted some aspects of PECNET implementations. The major contributions of the proposed method can be seen in improving the prediction accuracy for distinct types of time series data (chaotic and stochastic) using multiple neural networks where the secondary network is trained by shifted time series prediction error of the primary network. The overfitting is avoided due to an increase in recurrence-related feedback. The same structure and hyperparameter sets are applied for a wide range of time series prediction problems with moving frames in multiple scales. The discrete wavelet transform (DWT) used for preprocessing the input data yields better accuracy improvement than directly applying the time series data to the neural network in predictive error compensation. The PECNET for the stock price prediction problem is introduced in the second part of the dissertation. The selected data represent the non-stationary time series data. Due to the difficulties in the traditional normalization techniques that deal with non-stationary time series data, the average normalization method is proposed. The average value of the current input to the neural networks is computed and subtracted from particular input data. The proposed normalization method is able to represent the different volatilities and preservation of the original properties within each input sequence. The different frequencies of stock price time series data are used together in one neural network, while an additional network uses the previous residual errors as inputs. The updated learning method is applied in this part, enhancing the overall prediction performance. In the third part, the improved PECNET model enables choosing orthogonal features in data fusion applications. Different data types can be fused into one single model by extracting valuable knowledge from multivariate time series data. The extraction of valuable knowledge is done by checking the correlation between the remaining features and residual error. The PECNET chooses the highest correlating data to the residual error acquired by the previously trained network. It is well known that irrelevant features cause overfitting in forecasting models, representing a critical issue considering the number of samples and the number of available features. Because of that, selecting the proper feature set to the essential ones will reduce the learning process's computational cost and improve the accuracy by minimizing the overfitting. In the fourth chapter, the root-zone soil moisture problem is introduced. For this purpose, in-situ agrometeorological measurements and satellite remote sensing indices are used. The distance between the central point and known stations is calculated. The root-zone soil moisture estimation is done using only accumulated ground-based measurements as input data, using only remotely sensing indices, and combining both. Applying the PECNET to the spatio-temporal root-zone soil moisture estimation problem shows promising results. The results can be used to obtain the soil moisture map of neighboring points where sensor information is unavailable. The fifth part examines the decomposition into frequency bands of input time series data and the applicability of different filtering methods. For this purpose, the Butterworth filter is implemented and used as an additional filtering method. Besides the closing stock price as input data, the Far-Eastern stock market indices to obtain the spatial dimensional for the financial time series forecasting example has been included. The overall results showed that fusing spatial and temporal data together into a separately trained cascaded PECNET model can achieve promising results without causing overfitting or reducing the model performances. The proposed wavelet preprocessed PECNET also leaves room for improvement using various preprocessing techniques as well as different types of neural networks.
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ÖgeHierarchical deep bidirectional self-attention model for recommendation(Graduate School, 2023-05-02)This study proposes a bidirectional recommendation model to tackle the user cold start problem. We can predict the middle item when a user has only a few user-item interactions and enrich their interaction set accordingly. By recursively repeating this process, we can obtain enough interactions to make accurate item recommendations to the user. For instance, a user may buy a few items from an e-commerce site but also purchase other items from elsewhere, leading to incomplete information about their preferences. The proposed bidirectional recommendation model can fill the user's interaction history gaps, enabling accurate item recommendations even with limited data. In this thesis, we aimed to develop a recommendation system that imitates the behavior of today's e-commerce users' online purchasing experience. Our approach emphasized practicality, as we aimed to create a system that could be implemented easily in real-world e-commerce platforms. In doing so, we focused on developing an approach that could handle a large number of users and items found on such platforms while still maintaining high performance. By prioritizing these factors, we aimed to create a recommendation system that could be effectively applied in real-world scenarios. For future work, exploring combinations of the suggested algorithms for both layers would be worthwhile. Furthermore, examining the impact of algorithms proposed for the first layer or the user's shopping history enrichment algorithm on different recommendation systems would be beneficial. Ultimately, the most significant improvement is the application of proposed hierarchical recommendation network to cross-domain recommendation problems.
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ÖgeDirectional regularization based variational models for image recovery(Graduate School, 2022-08-19)This thesis explores how local directional cues can be utilized in image recovery. Our intent is to provide image regularization paradigms that encourage the underlying directionalities. To this end, in the first phase of the thesis work, we design direction-aware analysis-based regularization terms. We boost the structure tensor total variation (STV) functionals used in inverse imaging problems so that they encode directional priors. More specifically, we suggest redefining structure tensors to describe the distribution of the ``directional" first-order derivatives within a local neighborhood. With this decision, we bring direction-awareness to the STV penalty terms, which were originally imposing local structural regularity. We enrich the nonlocal counterpart of the STV in the same way, which were additionally imposing nonlocal image self-similarity beforehand. These two types of regularizers are used to model denoising and deblurring problems within a variational framework. Since they result in convex energy functionals, we also develop convex optimization algorithms by devising the proximal maps of our direction-aware penalty terms. With these contributions in place, the major barrier in making these regularizers applicable lies in the difficulty of estimating directional parameters (i.e., the directions/orientations, the dose of anisotropy). Although, it is possible to come across uni-directional images, the real-world images usually exhibit no directional dominance. It is easy to precisely estimate the underlying directions of uni-directional (or partially directional) images. However, arbitrary and unstable directions call for spatially varying directional parameters. In this regard, we propose two different parameter estimation procedures, each of which employs the eigendecompositions of the semi-local/nonlocal structure tensors. We also make use of total variation (TV) regularization in one of the proposed procedures and a filterbank of anisotropic Gaussian kernels (AGKs) in the other. As our image regularization frameworks require the guidance of the directional parameter maps, we use the term ``direction-guided" in naming our regularizers. Through the quantitative and the visual experiments, we demonstrate how beneficial the involvement of the directional information is by validating the superiority of our regularizers over the state-of-the-art analysis-based regularization schemes, including STV and nonlocal STV. In the second phase of the thesis, we shift our focus from model-driven to data-driven image restoration, more specifically we deal with transfer learning. As the target field, we choose fluorescence microscopy imaging, where noise is a very usual phenomenon but data-driven denoising is less applicable due to lack of the ground-truth images. In order to tackle this challenge, we suggest tailoring a dataset by handpicking images from unrelated source datasets. This selective procedure explores some low-level view-based features (i.e., color, isotropy/anisotropy, and directionality) of the candidate images, and their similarities to those of the fluorescence microscopy images. Based upon our experience on the model-driven restoration techniques, we speculate that these low-level characteristics (especially directions) play an important role on image restoration. In order to encourage a deep learning model to exploit these characteristics, one could embed them into the training data. In fact, we establish the possibility of offering a good balance between content-awareness and universality of the model by transferring only low-level knowledge and letting the unrelated images bring additional knowledge. In addition to training a feed-forward denoising convolutional neural network (DnCNN) on our tailored dataset, we also suggest integrating a small amount of fluorescence data through the use of fine-tuning for better-recovered micrographs. We conduct extensive experiments considering both Gaussian and mixed Poisson-Gaussian denoising problems. On the one hand, the experiments show that our approach is able to curate a dataset, which is significantly superior to the arbitrarily chosen unrelated source datasets, and competitive against the real fluorescence images. On the other hand, the involvement of fine-tuning further boosts the performance by stimulating the content-awareness, at the expense of a limited amount of target-specific data that we assume is available.
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ÖgeArtificial intelligence based and digital twin enabled aeronautical AD-HOC network management(Graduate School, 2022-12-20)The number of passengers using aircraft has been increasing gradually over the following years. With the increase in the number of passengers, significant changes in their needs have been made. In-flight connectivity (IFC) has become a crucial necessity for passengers with the evolving aeronautical technology. The passengers want to connect to the Internet without interruption regardless of their location and time. The aeronautical networks attract the attention of both industry and academia due to these reasons. Currently, satellite connectivity and air-to-ground (A2G) networks dominate existing IFC solutions. However, the high installation/equipment cost and latency of the satellites reduce their efficiency. Also, the terrestrial deployment of A2G stations reduces the coverage area, especially for remote flights over the ocean. One of the novel solutions is the Aeronautical Ad-hoc Networks (AANETs) to satisfy the IFC's huge demand by also solving the defects of satellite and A2G connectivities. The AANETs are based on creating air-to-air (A2A) links between airplanes and transmitting packets over these connections to enable IFC. The AANETs dramatically increase the Internet access rates of airplanes by widening the coverage area thanks to these established A2A links. However, the mobility and atmospheric effects on AANETs increase the A2A link breakages by leading to frequent aircraft replacement and reducing link quality. Accordingly, the mobility and atmospheric effects create the specific characteristics for AANETs. More specifically, the ultra-dynamic link characteristics of high-density airplanes create an unstructured and unstable topology in three-dimensional space for AANETs. To handle these specific characteristics, we first form a more stable, organized, and structured AANET topology. Then, we should continuously enable the sustainability and mapping of this created AANET topology by considering broken A2A links. Finally, we can route the packets over this formed, sustained, and mapped AANET topology. However, the above-explained AANET-specific characteristics restrict the applicability of conventional topology and routing management algorithms to AANET by increasing its complexity. More clearly, the AANET specific characteristics make its management challenging by reducing the packet delivery success of AANET with higher transfer delay. At that point, artificial intelligence (AI)-based solutions have been adapted to AANET to cope with the high management complexity by providing intelligent frameworks and architectures. Although AI-based management approaches are widely used in terrestrial networks, there is a lack of a comprehensive study that supports AI-based solutions for AANETs. Here, the AI-based AANET can take topology formation, sustainability, and routing management decisions in an automated fashion by considering its specific characteristics thanks to learning operations. Therefore, AI-based methodologies have an essential role in handling the management complexity of this hard-to-follow AANET environment as they support intelligent management architectures by also overcoming the drawbacks of conventional methodologies. On the other hand, these methodologies can increase the computational complexity of AANETs. At that point, we propose the utilization of the Digital Twin (DT) technology to handle computational complexity issues of AI-based methodologies. Based on these, in this thesis, we aim to propose an AI-based and DT-enabled management for AANETs. This system mainly consists of four main models as AANET Topology Formation Management, AANET Topology Sustainability Management, AANET Topology Mapping Management, and AANET Routing Management. Here, our first aim is to form a stable, organized, and structured AANET topology. Then, we will enable the sustainability of this formed topology. We also continuously map the formed and sustained AANET topology to airplanes. Finally, the packets of airplanes are routed on this formed, sustained, and mapped AANET topology. We will create these four models with different AI-based methodologies and combine all of them under the DT technology in the final step. In the Topology Formation Management, we will propose a three-phased topology formation model for AANETs based on unsupervised learning. The main reason for proposing an unsupervised learning-based algorithm is that we have independently located airplanes with unstructured characteristics in AANETs before forming the topology. They could be considered as the unlabeled training data for unsupervised learning. This management model utilizes the spatio-temporal locations of aircraft to create a more stable, organized, and structured AANET topology in the form of clusters. More clearly, the first phase corresponds to the aircraft clustering formation, and here, we aim to increase the AANET stability by creating spatially correlated clusters. The second phase consists of the A2A link determination for reducing the packet transfer delay. Finally, the cluster head selection increases the packet delivery ratio in AANET. In the Topology Sustainability Management, we will propose a learning vector quantization (LVQ) based topology sustainability model for AANETs based on supervised learning. The main reason for proposing a supervised learning-based algorithm is that we already have an AANET topology before the A2A link breakage, and we can use it in supervised learning for training. Accordingly, we can consider the clusters in AANET topology as a pattern; then, we can find the best matching cluster of an aircraft observing A2A link breakages through pattern classification instead of creating topology continuously. This management model works in three phases: winning cluster selection, intra-cluster link determination, and attribute update to increase the packet delivery ratio with reduced end-to-end latency. In the Topology Mapping Management, we will propose a gated recurrent unit (GRU) based topology mapping model for AANETs. In topology formation, we create AANET topology in the form of clusters by collecting airplanes having similar features under the same set. In topology sustainability, we sustain the formed clustered-AANET topology with supervised learning. However, these formed and sustained AANET topologies must be continuously mapped to the clustered airplanes to notify them about the current situation. This procedure could be considered a part of sustainability management. Here, we continuously notify the airplanes with GRU at each timestamp about topological changes. This management model works in two main parts ad forget and update gates. In Routing Management, we propose a q-learning (QLR) based routing management model for AANETs. For this aim, we map the AANET environment to reinforcement learning. Here, the QLR-based management model aims to let the airplanes find their routing path through exploration and exploitation. Accordingly, the routing algorithm can adapt to the dynamic conditions of AANETs. In this management model, we adapt the Bellman Equation to the AANET environment by proposing different methodologies for its related QLR components. Accordingly, this model mainly consists of two main parts current state & maximum state-action determination and dynamic reward determination. Therefore, we execute the topology formation, sustainability, and routing management modules through unsupervised, supervised, and reinforcement learning-based algorithms. Additionally, we take advantage of neural networks in topology mapping management. After managing the topology and routing of AANETs with AI-based models, in the DT-enabled AANET management, we will support them with the DT technology. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time to solve the computational challenges of AI-based methodologies. Therefore, we will introduce the utilization of DT technology for the AANET orchestration and propose a DT-enabled AANET (DT-AANET) management model. This model consists of the Physical AANET Twin and Controller, including the Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment. Also, the operational module executes the implemented AI-based models. Therefore, in this thesis, we aim to propose an AI-based and DT-enabled management for AANETs. In this management system, we will first aim to propose AI-based methodologies for AANET topology formation, topology sustainability, topology mapping, and routing issues. Then, we will support these AI-based methodologies with DT technology. This proposed complete management model increased the packet delivery success of AANETs with reduced end-to-end latency.
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ÖgeNovel data partitioning and scheduling schemes for dynamic federated vehicular cloud(Graduate School, 2022-08-23)In the last decade, many Intelligent Transport Systems (ITS) applications, that rely on the Internet cloud infrastructure have been deployed to provide services to clients in vehicular environments. However, the Internet cloud suffers from high latency in service access and intermittent Internet disconnection in vehicular environments. In response to these challenges, researchers have proposed the use of other distributed computing technologies such as mobile cloud computing, edge computing, and vehicular cloud computing which may rely on Wireless Local Area Networks (WLAN) or Ad-hoc networks for communication to provide computing resources and services in vehicular environments. Vehicular cloud computing, an emerging distributed computing paradigm that provides cloud services using the resources embedded in vehicles and Road Side Units (RSUs), is arguably the best alternative to other distributed computing systems because of its advantages. The provision of vehicular cloud services leads to efficient utilization of the abundant resources in embedded vehicles and therefore embraces green technology in vehicular environments. Considering that modern vehicles and RSUs have been designed with communication capabilities through the use of the Vehicular Ad-hoc Network (VANET), vehicular cloud computing may not require additional communication hardware infrastructure installation along roads and at parking lots before its deployment. Furthermore, access to vehicular cloud services may involve a relatively lower latency and fewer intermittent disconnections due to the close proximity of vehicular cloud resources to clients and the use of VANET which may provide better connections among vehicular resources and clients than the connection to Internet cloud in vehicular environments. As with all emerging technologies of which vehicular cloud is not an exception, some challenges need to be addressed before its full real-world deployment. The challenges of vehicular cloud computing are mainly caused by the unique characteristics of vehicular resources and the limited communication bandwidth of VANET. In vehicular environments, i.e., on roads and parking lots, vehicles are not permanently stationed but mobile, making the resources embedded in vehicles highly mobile. Also, the resources of vehicles that are organized to provide cloud services belong to different people (distributed ownership). The high mobility and distributed ownership of resources imply that vehicular resources may exit the provisioned vehicular cloud abruptly by either withdrawal of resources by the resource owner or intermittent network disconnection caused by the high mobility of vehicles. Therefore distributed ownership of resources and high mobility of vehicles cause vehicular resources to be volatile, making their availability and reliability unpredictable. The capacity constraint of the communication bandwidth of VANET, which serves as a communication backbone of vehicular cloud, implies that the transmission of data-intensive and bandwidth-intensive applications in vehicular environments is a challenge in vehicular cloud computing. Considering that the distributed ownership of resources, limited communication bandwidth, and high mobility of vehicles lead to low availability and low reliability of resources in vehicular cloud computing, they adversely affect almost all resource management operations, such as virtual machine migration. Therefore, the main focus of this dissertation is to propose novel solutions to address the challenges of vehicular cloud and ameliorate the adverse effects of the identified characteristics of vehicular cloud computing. As an introduction, the background of vehicular cloud computing and a detailed survey of resource management operations in vehicular cloud are presented using a three-phase taxonomy of resource management techniques proposed in this dissertation. Based on the review of vehicular cloud computing concepts, a novel distributed vehicular computing paradigm, Vehicular Volunteer Computing (VVC), is proposed in this dissertation. VVC is a volunteer computing platform where vehicle owners may donate their idle processing units towards the execution of scientific and other projects that are beneficial to a community, such as the "Compute The Cure" cancer project. The concept of a Dynamic Federated Vehicular Cloud (DFVC) is introduced in this dissertation to overcome the challenge of limited resource capacity and volatility of resources in vehicular cloud. The DFVC entails the organization of resources from different vehicles moving on the road to provide a specific vehicular cloud service such as Computation-as-a-Service (CaaS). Although the resources are collated from different vehicles in the formation of DFVC, they are organized as a single logical unit for the provision of services. The formation of a DFVC involves forming resource-based clusters, i.e., grouping vehicles with similar resource and mobility characteristics as a single unit and selecting a leader, known as a cluster head, to manage the resources in a cluster of vehicles. By considering the structure of resource-based clusters formed in a Region of Interest (RoI), two different DFVC schemes are proposed in this dissertation: the Cluster-Based Vehicular Cloud (CBVC) and the Platoon-Based Vehicular Cloud (PBVC). In the CBVC, vehicles (owners) with a high reputation and idle resources on an RoI on the road are organized into clusters without adhering to the condition that all cluster members use the same lane and maintain a fixed gap between vehicles. The PBVC, on the other hand, is a variant of the cluster-based vehicular cloud that requires strict adherence to a constant gap, i.e., the distance between all vehicles and the use of the same lane throughout the entire period of the provision of cloud services. In other words, the PBVC is a convoy with a constant gap between all vehicles whose resources are organized to offer specific cloud services. In order to address the challenge of limited computation capacity of resources and constraint communication bandwidth, a large divisible data load to be processed by the DFVC is partitioned and distributed to the individual vehicular nodes using efficient Data partitioning and Scheduling (DPS) schemes. One of the central themes of this dissertation is, therefore, to design and implement novel DPS schemes that consider the characteristics of vehicular resources of the DFVC and the communication channel of VANET: the communication backbone for DFVC. By considering the computation capacity of resources, data transmission bandwidth capacity, and communication delay experienced in data transmission in VANET, efficient DPS schemes proposed in this dissertation are designed through mathematical models developed using timing and data flow diagrams. The DPS schemes for the CBVC, and PBVC are modeled differently because of their unique characteristics and operations. The proposed DPS scheme for the CBVC was modeled with the consideration that the cluster head determines the data chunk for each vehicle using derived closed form mathematical equations and then distributes the data chunks directly as a single hop to the respective vehicles in the CVBC to process. After processing, the vehicles then transmit the processed data chunks directly to the cluster head. The DPS scheme for the CBVC is implemented as part of a unified data, resource, and channel management framework, which is referred to as the UniDRM in this dissertation. Considering different criteria or objectives for data partitioning and scheduling, three distinct DPS schemes, time-aware, cost-aware, and reliability-aware, are also proposed in this dissertation. For the PBVC, the data partitioning is carried out by the lead node, i.e., the first node of the platoon, using derived closed form equations. The determined data chunks of the platoon members are then distributed either directly (single hop) or through multi-hop transmission. The closed form equations were derived considering data flow and timing diagrams designed based on how vehicles in platoons exchange information with their neighbor nodes, which is referred to as platoon Information Flow Topologies (IFT). In this dissertation, existing platoon IFTs: the Bi-Directional (BD), Bi-Directional Lead (BDL), and the All- to- All IFT (A2A) are modified to derive mathematical models for six different DPS schemes, namely, the Bi-Directional-Recursive (BD-R), Bi-Directional Interlaced (BD-I), Bi-Directional Lead-Recursive (BDL-R), Bi-Directional Lead-Interlaced (BDL-I), Bi-Directional Lead Aggregate-Recursive (BDLA-R), and Bi-Directional Lead Aggregate-Interlaced (BDLA-I). Through realistic simulations developed via the use of the simulation platforms Omnet++, Sumo, Veins, and Plexe, a detailed performance analysis of the proposed DPS schemes were carried out. By developing the different DPS schemes for the PBVC, one of the long-standing challenges of divisible data partitioning and scheduling in the literature: the modeling and derivation of closed form equations for determining the percentage of data chunks and the processing time of the linearly arranged network of connected heterogeneous processors, is addressed. According to the literature, there are no closed form equations for the heterogeneous linear network of connected processors because of the complicated combinatorial terms that appear in expressions for individual data partitions while solving the recursive equations. However, using algebraic manipulations, closed form equations have been derived and modeled in this dissertation. In all, this dissertation presents novel solutions to key challenges of vehicular cloud computing, including the limited available capacity of resources in vehicles and the bandwidth constraint of the vehicular communications channel.
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