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Articles 3421 - 3450 of 3475
Full-Text Articles in Computer Sciences
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) …
Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Turkish Journal of Electrical Engineering and Computer Sciences
Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites, and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving their performance. In this study, prediction performances of neural networks are evaluated and their performances are improved using genetic algorithms. Performances obtained in this study are compared with those of other studies. After that, superiority …
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 …
Time-Oriented Interactive Process Miner: A New Approach For Time Prediction, İsmai̇l Yürek, Derya Bi̇rant, Özlem Ece Yürek, Kökten Ulaş Bi̇rant
Time-Oriented Interactive Process Miner: A New Approach For Time Prediction, İsmai̇l Yürek, Derya Bi̇rant, Özlem Ece Yürek, Kökten Ulaş Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
Everyday information systems collect a different kind of process instances of a business flow. As time goes on, the size of the collected data builds up speedily and constitutes a huge amount of data. It is a very challenging task to obtain valuable information and features of processes from such big data. Considering in advance, the trend and different features of the ongoing process are essential. Especially, time management is crucial in designing and conducting business processes. In this article, a novel process miner algorithm is proposed for time prediction, named time-oriented İnteractive process miner (T-IPM), which predicts the remaining …
Engraved Digit Detection Using Hog-Real Adaboost And Deep Neural Network, Tuan Linh Dang, Thang Cao, Yukinobu Hoshino
Engraved Digit Detection Using Hog-Real Adaboost And Deep Neural Network, Tuan Linh Dang, Thang Cao, Yukinobu Hoshino
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a framework for recognizing sequences of digits engraved on steel plates. These digits are normally blurred, dirty, not clear, tilted, and sometimes overlapped by other digits. Several digits in a string with uneven spacing and different sizes are detected at the same time. The framework consists of two main components called histogram of oriented gradient-real AdaBoost module and deep neural network module. The first component is used to detect digit windows, and the second component is employed to recognize digits inside the detected windows. Experimental results demonstrated that the proposed framework could be a potential solution to …
Abnormal Behavior Detection Using Sparse Representations Through Sequentialgeneralization Of K-Means, Ahlam Aldhamari, Rubita Sudirman, Nasrul Humaimi Mahmood
Abnormal Behavior Detection Using Sparse Representations Through Sequentialgeneralization Of K-Means, Ahlam Aldhamari, Rubita Sudirman, Nasrul Humaimi Mahmood
Turkish Journal of Electrical Engineering and Computer Sciences
The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems. This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors. In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences. The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene. The sequential generalization of k-means …
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 …
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 …
Deep-Learning-Based Spraying Area Recognition System Forunmanned-Aerial-Vehicle-Based Sprayers, Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmed Khan, Shahzad Anwer
Deep-Learning-Based Spraying Area Recognition System Forunmanned-Aerial-Vehicle-Based Sprayers, Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmed Khan, Shahzad Anwer
Turkish Journal of Electrical Engineering and Computer Sciences
Unmanned aerial vehicle (UAV)-based spraying system employing machine learning techniques is a recent advancement in precision agriculture for precise spraying, promoting saving chemicals (pesticide/herbicide), and enhancing their effectiveness. This study aims to develop an efficient deep learning system for UAV-based sprayers, which has the capability to accurately recognize spraying areas. A deep learning system is proposed and developed incorporating a faster region-based convolutional neural network (R-CNN) for the imagery collected. In order to develop a classifier for identifying spraying areas from nonspraying areas, four different agriculture croplands and orchards were considered. All the experiments were performed in agriculture fields through …
A Novel Optimum Pi Controller Design Based On Stability Boundary Locussupported Particle Swarm Optimization In Avr System, Mahmut Temel Özdemi̇r
A Novel Optimum Pi Controller Design Based On Stability Boundary Locussupported Particle Swarm Optimization In Avr System, Mahmut Temel Özdemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
This study proposes a new approach that combines stability and optimization in the design of proportional? integral (PI) controller of automatic voltage regulators (AVR) of synchronous generators with variable system parameters. Thanks to this approach, a PI controller, providing the desired performance and the stability of the AVR system, has been designed. The approach follows a method investigating the PI gain values to achieve the desired goals. In the first step of the study, a new stability boundary locus is calculated for the case in which AVR system?s parameters have changed. The stability boundary locus (SBL) method is a graphic-based …
Performance Evaluation Of A Power Allocation Algorithm Based On Dynamicblocklength Estimation For Urllc In The Multicarrier Downlink Noma Systems, Won Jae Ryu, Soo Young Shin
Performance Evaluation Of A Power Allocation Algorithm Based On Dynamicblocklength Estimation For Urllc In The Multicarrier Downlink Noma Systems, Won Jae Ryu, Soo Young Shin
Turkish Journal of Electrical Engineering and Computer Sciences
This study investigates a power allocation algorithm using blocklength estimation by the finite blocklength (FBL) regime in a multicarrier downlink nonorthogonal multiple access (NOMA) system for ultrareliable low latency communication (URLLC) that is one of the services in 5G networks, requiring exceedingly high reliability and low latency. As NOMA systems can boost the capacity and increase the spectrum efficiency, it can be considered as a solution for URLLC. A multicarrier downlink NOMA system using blocklength estimation based on the FBL regime is proposed for effective resource allocation in this study. The FBL is used to derive the equation for dynamic …
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 …
A Novel Data Placement Strategy To Reduce Data Traffic During Run-Time, Sridevi Sridhar, Rhymend Uthariaraj Vaidyanathan
A Novel Data Placement Strategy To Reduce Data Traffic During Run-Time, Sridevi Sridhar, Rhymend Uthariaraj Vaidyanathan
Turkish Journal of Electrical Engineering and Computer Sciences
High impact scientific applications processed in distributed data centers often involve big data. To avoid the intolerable delays due to huge data movements across data centers during processing, the concept of moving tasks to data was introduced in the last decade. Even after the realization of this concept termed as data locality, the expected quality of service was not achieved. Later, data colocality was introduced where data groupings were identified and then data chunks were placed wisely. However, the aspect of the expected data traffic during run time is generally not considered while placing data. To identify the expected data …
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 …
Neurofuzzy Robust Backstepping Based Mppt Control For Photovoltaic System, Kamran Ali, Laiq Khan, Qudrat Khan, Shafaat Ullah, Naghmash Ali
Neurofuzzy Robust Backstepping Based Mppt Control For Photovoltaic System, Kamran Ali, Laiq Khan, Qudrat Khan, Shafaat Ullah, Naghmash Ali
Turkish Journal of Electrical Engineering and Computer Sciences
Linear maximum power point tracking (MPPT) techniques are unable to achieve the desired performance and efficiency under wide variation in atmospheric conditions (temperature and irradiance) and consequently the maximum power point (MPP). Hence, the design and implementation of a nonlinear MPPT controller is essential to address the problems associated with the variations of the MPP. In this research article, a new nonlinear robust backstepping-based MPPT control technique is proposed for a standalone PV array connected to a dynamic load, and its performance comparison with existing backstepping, integral backstepping and conventional proportional integral derivative (PID) and perturb and observe (P&O) based …
Performance Improvement Of The Shunt Active Power Filter Using A Novel Adaptivefiltering Approach, Abderrezzaq Zoghbi, Daoud Berkani
Performance Improvement Of The Shunt Active Power Filter Using A Novel Adaptivefiltering Approach, Abderrezzaq Zoghbi, Daoud Berkani
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces an efficient control approach to enhance harmonics mitigation performance of the shunt active power filter (SAPF). This approach is based on adaptive filters favored by their built-in automatic parameters adaptation capability. The proposed filter, which uses a variable leaky least mean square (VLLMS) adaptation, is applied with a modified instantaneous power PQ theory. This enhances its dynamic performance over the use of conventional time-invariant filters, and overcomes its limitations in the presence of nonsinusoidal voltage conditions. The studied SAPF model is simulated in MATLAB/SIMULINK with combinations of nonlinear and unbalanced loads. Simulation results indicate a significant improvement …
Robust Model Reference Adaptive Pi Controller Based Sliding Mode Control Forthree-Phase Grid Connected Photovoltaic Inverter, Mojtaba Moeti, Mehdi Asadi
Robust Model Reference Adaptive Pi Controller Based Sliding Mode Control Forthree-Phase Grid Connected Photovoltaic Inverter, Mojtaba Moeti, Mehdi Asadi
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, the design of a new robust model reference adaptive PI (MRAC-PI) current controller is proposed for a two-stage grid connected photovoltaic (PV) inverter. Perturb & observe (P&O) algorithm is implemented in the boost part in order to extract the maximum power from the PV array. Firstly, the current dynamics with considering the system uncertainties are written in dq frame and are simplified by employing a decoupling system. Then, the MRAC-PI controller is designed based on a sliding mode control (SMC) to improve the robustness of the controller under system uncertainties. The parameters of PI controller are tuned …
A Counter Based Approach For Reducer Placement With Augmented Hadoop Rackawareness, Mir Wajahat Hussain, K Hemant Reddy, Diptendu Sinha Roy
A Counter Based Approach For Reducer Placement With Augmented Hadoop Rackawareness, Mir Wajahat Hussain, K Hemant Reddy, Diptendu Sinha Roy
Turkish Journal of Electrical Engineering and Computer Sciences
As the data-driven paradigm for intelligent systems design is gaining prominence, performance requirements have become very stringent, leading to numerous fine-tuned versions of Hadoop and its MapReduce programming model. However, very few researchers have investigated the effect of intelligent reducer placement on Hadoop's performance. This paper delves into this much ignored reducer placement phase for improving Hadoop's performance and proposes to spawn reduce phase of Hadoop tasks in an asynchronous fashion across nodes in a Hadoop cluster. The main contributions of this paper are: (i) to track when map phase of tasks are completed, (ii) to count the number of …
A Two-Stage Hair Region Localization Method For Guided Laser Hair Removal, Murat Avşar, İmam Şami̇l Yeti̇k
A Two-Stage Hair Region Localization Method For Guided Laser Hair Removal, Murat Avşar, İmam Şami̇l Yeti̇k
Turkish Journal of Electrical Engineering and Computer Sciences
Removal of hair using laser is a widely used method, where our goal is to permanently remove hair by using laser to cause heat in order to thermally damage the hair follicle. However, currently available laser hair removal systems affect the outer skin layers besides hair follicles. This is a disadvantage of classical methods with major health risks. We propose a method to overcome these health risks by guiding the laser beam only to automatically localized hair regions. This study aims to develop an automated feature-based hair region localization method as an integral part of the proposed hair removal system …
Artificial Intelligence As Evidence, Paul W. Grimm, Maura R. Grossman, Gordon V. Cormack
Artificial Intelligence As Evidence, Paul W. Grimm, Maura R. Grossman, Gordon V. Cormack
Faculty Scholarship
This article explores issues that govern the admissibility of Artificial Intelligence (“AI”) applications in civil and criminal cases, from the perspective of a federal trial judge and two computer scientists, one of whom also is an experienced attorney. It provides a detailed yet intelligible discussion of what AI is and how it works, a history of its development, and a description of the wide variety of functions that it is designed to accomplish, stressing that AI applications are ubiquitous, both in the private and public sectors. Applications today include: health care, education, employment-related decision-making, finance, law enforcement, and the legal …
Virtual Tutor Personality In Computer Assisted Language Learning, Johanna Dobbriner, Cathy Ennis, Robert J. Ross
Virtual Tutor Personality In Computer Assisted Language Learning, Johanna Dobbriner, Cathy Ennis, Robert J. Ross
Conference papers
The use of intelligent virtual agents in language learning has increased in recent years. Studies into several aspects of personalisation aiming to increase user engagement are an ongoing research topic with avatar personality being one such aspect. As a step towards our development of intelligent virtual avatars, we present two of our initial experiments to explore differences in user interaction with two contrasting avatar personalities -- P1: open-minded, friendly and sociable and P2: closed-off, curt and distant. Each user interacted with a single personality in a video-call setting and gave feedback on the interaction. Our expectations, that P1 would be …
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: …
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 …
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 …
Cybersecurity Leaders: Knowledge Driving Human Capital Development, Sharon L. Burton
Cybersecurity Leaders: Knowledge Driving Human Capital Development, Sharon L. Burton
Publications
Cybersecurity leaders must be able to use critical reading and thinking skills, exercise judgment when policies are not distinct and precise, and have the knowledge, skills, and abilities to tailor technical and planning data to diverse customers’ levels of understanding. Ninety-three percent of cybersecurity leaders do not report directly to the chief operating officer. While status differences influence interactions amid groups, attackers are smarter. With the aim of protecting organizations and reducing risk, knowledge about security must increase. Understanding voids are costly and increased breach chances are imminent. Burning questions exist. What are needed technological learnings for cybersecurity leaders to …
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna
Computer Science Faculty Publications
Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …
Automated Filtering Of Eye Movements Using Dynamic Aoi In Multiple Granularity Levels, Gavindya Jayawardena, Sampath Jayarathna
Automated Filtering Of Eye Movements Using Dynamic Aoi In Multiple Granularity Levels, Gavindya Jayawardena, Sampath Jayarathna
Computer Science Faculty Publications
Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object …