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Articles 19381 - 19410 of 63083
Full-Text Articles in Computer Sciences
Artificial Intelligence And Soft Computing In Smart Structural Systems, Sajad Javadinasab Hormozabad
Artificial Intelligence And Soft Computing In Smart Structural Systems, Sajad Javadinasab Hormozabad
Theses and Dissertations--Civil Engineering
Next-generation smart cities are the key feature in the next chapter of human life. Cities that employ innovative and technology-driven solutions to improve the sustainability, resilience, prosperity, and amenity of the community are considered smart cities. Development of smart cities requires fundamental innovations in many technical and technological aspects including those contributing to smart structures. Smart technologies improve the structural performance against natural disasters like earthquakes, hurricanes, tornados, and promote the sustainability of structural systems. Next-generation smart structures encompass a variety of technologies including Structural Control (SC) and Structural Health Monitoring (SHM). SC covers methodologies and technologies that modify the …
Markov Decision Processes With Embedded Agents, Luke Harold Miles
Markov Decision Processes With Embedded Agents, Luke Harold Miles
Theses and Dissertations--Computer Science
We present Markov Decision Processes with Embedded Agents (MDPEAs), an extension of multi-agent POMDPs that allow for the modeling of environments that can change the actuators, sensors, and learning function of the agent, e.g., a household robot which could gain and lose hardware from its frame, or a sovereign software agent which could encounter viruses on computers that modify its code. We show several toy problems for which standard reinforcement-learning methods fail to converge, and give an algorithm, `just-copy-it`, which learns some of them. Unlike MDPs, MDPEAs are closed systems and hence their evolution over time can be treated as …
Multi-Stream Longitudinal Data Analysis Using Deep Learning, Sajjad Fouladvand
Multi-Stream Longitudinal Data Analysis Using Deep Learning, Sajjad Fouladvand
Theses and Dissertations--Computer Science
Longitudinal healthcare data encompasses all tasks where patients information are collected at multiple follow-up times. Analyzing this data is critical in addressing many real world problems in healthcare such as disease prediction and prevention. In this thesis, technical challenges in analyzing longitudinal administrative claims data are addressed and novel deep learning based models are proposed for multi-stream data analysis and disease prediction tasks. These algorithms and frameworks are assessed mainly on substance use disorders prediction tasks and specifically designed to tackled these disorders. Substance use disorder is a public health crisis costing the US an estimated $740 billion annually in …
Unmanned Aerial Vehicles In Opportunistic Networks, Salih Safa Bacanli
Unmanned Aerial Vehicles In Opportunistic Networks, Salih Safa Bacanli
Electronic Theses and Dissertations, 2020-2023
This dissertation presents novel algorithms for utilizing unmanned aerial vehicles (UAVs) through various scenarios within opportunistic networks. The opportunistic networks are considered challenging due to the intermittent and unreliable communication between nodes. UAVs can be used for delivering packets within opportunistic networks that can alleviate communication issues. We start examining the UAV usage in opportunistic networks by first investigating their effectiveness and proposing a UAV scanning approach. To validate the usage of UAVs, we evaluated the performance of an opportunistic network with and without using UAVs. The scanning techniques we investigated were random scan, meander scan, and our proposed approach …
Exploring Relationships Between Ground And Aerial Views By Synthesis And Matching, Krishna Regmi
Exploring Relationships Between Ground And Aerial Views By Synthesis And Matching, Krishna Regmi
Electronic Theses and Dissertations, 2020-2023
Cross-view images, referring to the images taken from aerial and street views, contain drastically differing representations of the same scene of a given location. Due to the differences in the camera viewpoints of ground and aerial images the same semantic concepts in the two viewpoints look very different. Therefore the problem of relating them is very challenging. Thus, it becomes crucial to explore the cross-view relations and learn appropriate representations such that images from these two domains can be associated. In this dissertation we explore the relationship between ground and aerial views by synthesis and matching. First, we explore supervised …
Test Overfitting In Automated Program Repair: Measurements And Approaches Using Formal Methods, Amirfarhad Nilizadeh
Test Overfitting In Automated Program Repair: Measurements And Approaches Using Formal Methods, Amirfarhad Nilizadeh
Electronic Theses and Dissertations, 2020-2023
Bugs exist in software systems; unfortunately, manually finding bugs and repairing them is complex, time-consuming, and expensive. Automated Program Repair (APR) techniques have promising results to make the debugging process automatic and dramatically decreasing the cost of developing a software system. Almost all developed APR tools use test suites to bug localization and evaluate generated candidate patches' correctness; thus, it is named dynamic APR. Test overfitting is one of the main challenges of dynamic APR tools, which is evident from several recent studies. Test overfitting means the repaired program is not correct based on the program's expected behavior while the …
Cyber Insurance Effects On Cyber Hygiene: Does The Homeostatic Effect Apply?, Wendi M. Kappers, Aaron Glassman, Michael S. Wills
Cyber Insurance Effects On Cyber Hygiene: Does The Homeostatic Effect Apply?, Wendi M. Kappers, Aaron Glassman, Michael S. Wills
Publications
A theoretical framework and research strategy is proposed to gain insight into perceptions and decisions as to how SMBs make decisions regarding cybersecurity hygiene measures, which could lead to betterinformed decisions regarding insurance as part of an ISA program, as well as have a bearing on policy structures and pricing for such insurance. This is because the definition of “cybersecurity hygiene habits”(CHH) as a task appears to vary within the industry and makes the practice hard to measure and evaluate. Research suggests that there may be a poorly understood connection between CHHs undertaken by organizations and their perceptions and/or adoption …
Real-Time Monitoring As A Supplementary Security Component Of Vigilantism In Modern Network Environments, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan
Real-Time Monitoring As A Supplementary Security Component Of Vigilantism In Modern Network Environments, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan
Research outputs 2014 to 2021
© 2020, The Author(s). The phenomenon of network vigilantism is autonomously attributed to how anomalies and obscure activities from adversaries can be tracked in real-time. Needless to say, in today’s dynamic, virtualized, and complex network environments, it has become undeniably necessary for network administrators, analysts as well as engineers to practice network vigilantism, on traffic as well as other network events in real-time. The reason is to understand the exact security posture of an organization’s network environment at any given time. This is driven by the fact that modern network environments do, not only present new opportunities to organizations but …
Privacy-Preserving Non-Participatory Surveillance System For Covid-19-Like Pandemics, Mahmoud Nabil, Ahmed Sherif, Mohamed Mahmoud, Waleed Alsmary, Maazen Alsabaan
Privacy-Preserving Non-Participatory Surveillance System For Covid-19-Like Pandemics, Mahmoud Nabil, Ahmed Sherif, Mohamed Mahmoud, Waleed Alsmary, Maazen Alsabaan
Faculty Publications
COVID-19 pandemic has revealed a pressing need for an effective surveillance system to control the spread of infection. However, the existing systems are run by the people’s smartphones and without a strong participation from the people, the systems become ineffective. Moreover, these systems can be misused to spy on people and breach their privacy. Due to recent privacy breaches, people became anxious about their privacy, and without privacy reassurance, the people may not accept the systems. In this paper, we propose a non-participatory privacy-preserving surveillance system for COVID-19-like pandemics. The system aims to control the spread of COVID-19 infection without …
Zero-Shot Action Recognition With Knowledge Enhanced Generative Adversarial Networks, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever
Zero-Shot Action Recognition With Knowledge Enhanced Generative Adversarial Networks, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever
Conference papers
Zero-Shot Action Recognition (ZSAR) aims to recognise action classes in videos that have never been seen during model training. In some approaches, ZSAR has been achieved by generating visual features for unseen classes based on the semantic information of the unseen class labels using generative adversarial networks (GANs). Therefore, the problem is converted to standard supervised learning since the unseen visual features are accessible. This approach alleviates the lack of labelled samples of unseen classes. In addition, objects appearing in the action instances could be used to create enriched semantics of action classes and therefore, increase the accuracy of ZSAR. …
Just-In-Time Biomass Yield Estimation With Multi-Modal Data And Variable Patch Training Size, Patricia O'Byrne, Patrick Jackman Dr., Damon Dr. Berry Dr., Thomas Lee, Michael French, Robert J. Ross
Just-In-Time Biomass Yield Estimation With Multi-Modal Data And Variable Patch Training Size, Patricia O'Byrne, Patrick Jackman Dr., Damon Dr. Berry Dr., Thomas Lee, Michael French, Robert J. Ross
Conference papers
The just-in-time estimation of farmland traits such as biomass yield can aid considerably in the optimisation of agricultural processes. Data in domains such as precision farming is however notoriously expensive to collect and deep learning driven modelling approaches need to maximise performance but also acknowledge this reality. In this paper we present a study in which a platform was deployed to collect data from a heterogeneous collection of sensor types including visual, NIR, and LiDAR sources to estimate key pastureland traits. In addition to introducing the study itself we address two key research questions. The first of these was the …
Interactive Learning Approach For Arabic Target-Based Sentiment Analysis, Husamelddin Balla, Marisa Llorens, Sarah Jane Delany
Interactive Learning Approach For Arabic Target-Based Sentiment Analysis, Husamelddin Balla, Marisa Llorens, Sarah Jane Delany
Conference papers
Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis. In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IALSTM model uses an interactive attentionbased mechanism to force the model to focus on different parts (targets) of a sentence. We investigate the ability to use targets, right and left contexts, and model them separately to learn their own representations via interactive modeling. We evaluated our model on two different datasets: …
K-Nearest Neighbour Classifiers - A Tutorial, Padraig Cunningham, Sarah Jane Delany
K-Nearest Neighbour Classifiers - A Tutorial, Padraig Cunningham, Sarah Jane Delany
Conference papers
Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of …
Pothole Detection Under Diverse Conditions Using Object Detection Model, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever
Pothole Detection Under Diverse Conditions Using Object Detection Model, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever
Datasets
One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalizable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles and resolutions. In this paper we present our …
Explainable Feature- And Decision-Level Fusion, Siva Krishna Kakula
Explainable Feature- And Decision-Level Fusion, Siva Krishna Kakula
Dissertations, Master's Theses and Master's Reports
Information fusion is the process of aggregating knowledge from multiple data sources to produce more consistent, accurate, and useful information than any one individual source can provide. In general, there are three primary sources of data/information: humans, algorithms, and sensors. Typically, objective data---e.g., measurements---arise from sensors. Using these data sources, applications such as computer vision and remote sensing have long been applying fusion at different "levels" (signal, feature, decision, etc.). Furthermore, the daily advancement in engineering technologies like smart cars, which operate in complex and dynamic environments using multiple sensors, are raising both the demand for and complexity of fusion. …
Towards Location-Independent Eyes-Free Text Entry, Dylan C. Gaines
Towards Location-Independent Eyes-Free Text Entry, Dylan C. Gaines
Dissertations, Master's Theses and Master's Reports
We propose an interface for eyes-free text entry using an ambiguous technique and conduct a preliminary user study. We find that user are able to enter text at 19.09 words per minute (WPM) with a 2.08% character error rate (CER) after eight hours of practice. We explore ways to optimize the ambiguous groupings to reduce the number of disambiguation errors, both with and without familiarity constraints. We find that it is feasible to reduce the number of ambiguous groups from six to four. Finally, we explore a technique for presenting word suggestions to users using simultaneous audio feedback. We find …
Light Field Compression And Manipulation Via Residual Convolutional Neural Network, Eisa Hedayati
Light Field Compression And Manipulation Via Residual Convolutional Neural Network, Eisa Hedayati
Dissertations, Master's Theses and Master's Reports
Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF's, using simulation, it is possible to explore a high-quality captured LF's properties. The immediate concerns following the LF capture are its storage and processing …
Modeling Human Visual Detection Using Deep Networks, Zach Dekraker
Modeling Human Visual Detection Using Deep Networks, Zach Dekraker
Dissertations, Master's Theses and Master's Reports
The work in this report describes the use of machine learning to model human visual detection. This is in contrast to typical machine learning models, which seek to optimize detection performance overall, e.g., precision versus recall or F1 scores. Instead the goal is to develop models that can accurately match humans' abilities to detect objects in images. There are many AI algorithms that have far surpassed humans in, for example, object detection in large image databases or games such as Go. What is different about this work is that the objective is to accurately model humans' performance in visual detection …
Wireless Sensing - Enabler Of Future Wireless Technologies, Hali̇se Türkmen, Muhammad Sohai̇b J. Solai̇ja, Armed Tusha, Hüseyi̇n Arslan
Wireless Sensing - Enabler Of Future Wireless Technologies, Hali̇se Türkmen, Muhammad Sohai̇b J. Solai̇ja, Armed Tusha, Hüseyi̇n Arslan
Turkish Journal of Electrical Engineering and Computer Sciences
Withthe completion of the 5G standardization efforts, the wireless communication world has now turned tothe road ahead, the future wireless communication visions. One common vision is that future networks will be flexible,or able to accommodate an even richer variety of services with stringent, often conflicting requirements. This ambitiousfeat can only be accomplished with a ubiquitous awareness of the radio and physical environment. To this end, thispaper highlights the importance of wireless sensing as a means for radio environment awareness and surveys wirelesssensing methods under different domains. Then, a review of wireless sensing from a standardization perspective is given.These standardization efforts …
Internet Of Things Data Compression Based On Successive Data Grouping, Samer Sawalha, Ghazi Al-Naymat
Internet Of Things Data Compression Based On Successive Data Grouping, Samer Sawalha, Ghazi Al-Naymat
Turkish Journal of Electrical Engineering and Computer Sciences
Internet of things (IoT) is a useful technology in different aspects, and it is widely used in many applications; however, this technology faces some major challenges which need to be solved, such as data management and energy saving. Sensors generate a huge amount of data that need to be transferred to other IoT layers in an efficient way to save the energy of the sensor because most of the energy is consumed in the data transmission process. Sensors usually use batteries to operate; thus, saving energy is very important because of the difficulty of replacing batteries of widely distributed sensors. …
Covid-19 Detection On Ibm Quantum Computer With Classical-Quantum Transferlearning, Erdi̇ Acar, İhsan Yilmaz
Covid-19 Detection On Ibm Quantum Computer With Classical-Quantum Transferlearning, Erdi̇ Acar, İhsan Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) …
Effects Of Covid-19 On Electric Energy Consumption In Turkey And Ann-Basedshort-Term Forecasting, Harun Özbay, Adem Dalcali
Effects Of Covid-19 On Electric Energy Consumption In Turkey And Ann-Basedshort-Term Forecasting, Harun Özbay, Adem Dalcali
Turkish Journal of Electrical Engineering and Computer Sciences
: Due to the coronavirus, millions of people worldwide carry out their work, education, shopping, culture, and entertainment activities from their homes now using the advantages of today's technology. Apart from this, patient care and follow-up are carried out with the help of electronic equipment especially in the institutions where health services are provided. It is important to provide a reliable electricity supply for humanity so that people can perform all these services. In this study, the outlook of energy in Turkey was examined. The current energy consumption and investments were examined. Then, the precautions by the government in the …
Application Of Fractional Order Pi Controllers On A Magnetic Levitation System, Erhan Yumuk, Müjde Güzelkaya, İbrahi̇m Eksi̇n
Application Of Fractional Order Pi Controllers On A Magnetic Levitation System, Erhan Yumuk, Müjde Güzelkaya, İbrahi̇m Eksi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Fractional order PI controllers based on two different analytical design methods are applied to a magnetic levitation system in this paper. The controller parameters are specified in order to fulfill specific frequency criteria. The first design method utilizes a unity feedback reference model whose forward path includes Bode's ideal loop transfer function. The second method uses the reference model that has been obtained via delayed Bode's ideal loop transfer function. The achievement of these two controllers are contrasted with each other on the magnetic levitation system using various criteria.
Development Of Computationally Efficient Biorthogonal Wavelets, Mehmet Cemi̇l Kale
Development Of Computationally Efficient Biorthogonal Wavelets, Mehmet Cemi̇l Kale
Turkish Journal of Electrical Engineering and Computer Sciences
Daubechies 5-tap/3-tap (Daub 5/3) wavelet and Kale 5-tap/3-tap (Kale 5/3) wavelet are computationally efficient wavelets which can be implemented by bitwise shifts and additions in the lifting scheme. In this work, presented is a formulation for computationally efficient wavelet prediction (P) and update (U) filters of two-channel lifting structures. Their subband decomposition scheme counterparts are also given. This research bases itself on the Daub 5/3 and Kale 5/3 wavelets and develops a formula for wavelets (which can be implemented with bitwise shifts and additions) that are derived from these two wavelets. The proposed wavelets are tried on 16 test images …
Characterization Of Different Crowd Behaviors Using Novel Deep Learningframework, Abdullah Jaman Alzahrani, Sultan Daud Khan
Characterization Of Different Crowd Behaviors Using Novel Deep Learningframework, Abdullah Jaman Alzahrani, Sultan Daud Khan
Turkish Journal of Electrical Engineering and Computer Sciences
Crowd behavior understanding is recognized as a complex problem due to unpredictable behavior of humans and complex interactions of individuals in groups. For crowd managers, it is crucial to understand the crowd dynamics to manage the crowd efficiently and effectively. Current practice of crowd management is based on manual analysis of the scene. Such manual analysis of the scene is a tedious job and usually prone to errors due to limited human capabilities. Therefore, the task of automatizing crowd analysis has received tremendous attention from the research community during the recent years. In this paper, we propose a deep model …
A New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar
A New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar
Turkish Journal of Electrical Engineering and Computer Sciences
The present study aims to introduce a robust model for distribution network expansion planning considering system uncertainties. The proposed method determines optimal size and placement of distributed generation resources, as well as installation and reinforcement of feeders and substations. This model is designed to minimize cost and to determine the best time for the installation of equipment in the expansion planning. In the proposed expansion planning, the fuzzy logic theory is employed to model uncertainties of loads and energy price. Also, since the proposed model is a nonlinear and nonconvex optimization problem, a tri-stage algorithm is developed to solve it. …
Real-Time Measurements And Performance Analysis Of Closed-Loop Mimo Servicefor Mobile Operators, Engi̇n Zeydan, Ömer Dedeoğlu, Yekta Türk
Real-Time Measurements And Performance Analysis Of Closed-Loop Mimo Servicefor Mobile Operators, Engi̇n Zeydan, Ömer Dedeoğlu, Yekta Türk
Turkish Journal of Electrical Engineering and Computer Sciences
As fifth generation (5G) networks are starting to become commercial, user expectations in terms of new services become high as well. This signifies that mobile communications service providers need to build robust 5G new services as quickly and cost-efficiently as possible. Many new technologies rely on closed-loop (CL) and multiple input multiple output (MIMO) technologies due to emerging cooperation between nodes in next generation networks. In this paper, we first compare different multiantenna transmission modes namely: transmit diversity, open-loop (OL), and CL MIMO spatial multiplexing strategies to provide mobile network operator (MNO) services in terms of their characteristics, ,limitations and …
Analyzing The Performances Of Evolutionary Multi-Objective Optimizers On Designoptimization Of Robot Gripper Configurations, Murat Dörterler, Ümi̇t Ati̇la, Rafet Durgut, İsmai̇l Şahi̇n
Analyzing The Performances Of Evolutionary Multi-Objective Optimizers On Designoptimization Of Robot Gripper Configurations, Murat Dörterler, Ümi̇t Ati̇la, Rafet Durgut, İsmai̇l Şahi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Robot grippers are widely used in a variety of areas requiring automation, precision, and safety. The performance of the grippers is directly associated with their design. In this study, four different multiobjective metaheuristic algorithms including particle swarm optimization (MOPSO), artificial algae algorithm (MOAAA), grey wolf optimizer (MOGWO) and nondominated sorting genetic algorithm (NSGA-II) were applied to two different configurations of highly nonlinear and multimodal robot gripper design problem including two objective functions and a certain number of constraints. The first objective is to minimize the difference between minimum and maximum forces for the assumed range in which the gripper ends …
A Novel Fibonacci Hash Method For Protein Family Identification By Usingrecurrent Neural Networks, Talha Burak Alakuş, İbrahi̇m Türkoğlu
A Novel Fibonacci Hash Method For Protein Family Identification By Usingrecurrent Neural Networks, Talha Burak Alakuş, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since proteins are highly used in smart drug therapies, protein functions, and, in some cases, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, a computer and artificial intelligence based classification system is needed to save time and avoid complexity in protein classification process. In order to designate the protein families …
Development Of An Intelligent Controller For Robot-Aided Assessment Andtreatment Guidance In Physical Medicine And Rehabilitation, Mehmet Emi̇n Aktan, Erhan Akdoğan
Development Of An Intelligent Controller For Robot-Aided Assessment Andtreatment Guidance In Physical Medicine And Rehabilitation, Mehmet Emi̇n Aktan, Erhan Akdoğan
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, an intelligent controller was developed for a rehabilitation robot called DIAGNOBOT, which can be used for assessment and treatment in the rehabilitation of wrist and forearm. The controller has a decision support system structure strengthened with conventional statistical methods and databases. The controller uses the patient's biomechanical parameters to make an assessment and proposes a treatment in line with this. In accordance with the recommended treatment, it produces the control parameters, torque, and position information for the control of the rehabilitation robot. The system's ability of assessment and treatment was tested with voluntary patients. Through these test …