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2019

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Articles 1531 - 1560 of 2060

Full-Text Articles in Computer Engineering

Use Of Virtual Reality Technology In Medical Training And Patient Rehabilitation, Sankalp Mishra Jan 2019

Use Of Virtual Reality Technology In Medical Training And Patient Rehabilitation, Sankalp Mishra

Browse all Theses and Dissertations

Coaching patients to follow the rehabilitation routines correctly and timely after surgery is often a challenge due to the limited medical knowledge of patients and limited availability of clinicians. Similarly, it is also a challenge to train medical professionals with both the technical and communication skills required in their practices. The recent emergence of VR technologies shines the light on improving the current training practices. In this thesis research, I will look at the development and application of VR-based immersive training games for two particular cases: 1. Post hand surgery rehab; and, 2. Training for Social determinants of health (SDOH) …

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Building A Classification Model Using Affinity Propagation, Christopher R. Klecker Jan 2019

Building A Classification Model Using Affinity Propagation, Christopher R. Klecker

College of Graduate Studies: Theses & Dissertations

Regular classification of data includes a training set and test set. For example for Naïve Bayes, Artificial Neural Networks, and Support Vector Machines, each classifier employs the whole training set to train itself. This thesis will explore the possibility of using a condensed form of the training set in order to get a comparable classification accuracy. The technique explored in this thesis will use a clustering algorithm to explore with data records can be labeled as exemplar, or a quality of multiple records. For example, is it possible to compress say 50 records into one single record? Can a single …

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Should Robots Prosecute And Defend?, Stephen E. Henderson Jan 2019

Should Robots Prosecute And Defend?, Stephen E. Henderson

Faculty Articles

Even when we achieve the ‘holy grail’ of artificial intelligence—machine intelligence that is at least as smart as a human being in every area of thought—there may be classes of decisions for which it is intrinsically important to retain a human in the loop. On the common account of American criminal adjudication, the role of prosecutor seems to include such decisions given the largely unreviewable declination authority, whereas the role of defense counsel would seem fully susceptible of automation. And even for the prosecutor, the benefits of automation might outweigh the intrinsic decision-making loss, given that the ultimate decision—by judge …

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Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari Jan 2019

Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …

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Blockchain-Based Healthcare: Three Successful Proof-Of-Concept Pilots Worth Considering, Rebecca Angeles Jan 2019

Blockchain-Based Healthcare: Three Successful Proof-Of-Concept Pilots Worth Considering, Rebecca Angeles

Journal of International Technology and Information Management

This paper features the use of blockchain technology in the healthcare industry, with special focus on healthcare data exchange and interoperability; drug supply chain integrity and remote auditing; and clinical trials and population health research. This study uses the research method of analyzing the published case studies, academic articles, trade articles, and videos on MEDRec, Patientory, and the AmerisourceBergen/Merck alliance with SAP/CryptoWerk. The “blockchain” concept was introduced around October 2008 when a proposal for the virtual currency, bitcoin, was offered. Blockchain is a much broader concept than bitcoin and has the following key attributes: distributed database; peer-to-peer transmission; transparency with …

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Car Image Classification Using Deep Neural Networks, Mingchen Li Jan 2019

Car Image Classification Using Deep Neural Networks, Mingchen Li

Honors Theses

Image classification is widely used in many fields of study. Deep neural networks are proven to be effective classifier structure due to its massive parameters and training capability. This paper outlines the development of Deep Neural Network in recent years and applied them on a Car image data set in order to compare their performances.

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ระบบการจัดการมูลค่าข้อมูลจากเกมสู่เกมด้วยบล็อกเชน, ชานน ยาคล้าย Jan 2019

ระบบการจัดการมูลค่าข้อมูลจากเกมสู่เกมด้วยบล็อกเชน, ชานน ยาคล้าย

Chulalongkorn University Theses and Dissertations (Chula ETD)

แม้ว่าในปัจจุบันบล็อกเชนจะถูกนำมาใช้ประโยชน์ในหลายอุตสาหกรรม แต่ในอุตสาหกรรมเกมนั้น บล็อกเชนไม่ได้ถูกนำไปใช้อย่างกว้างขวางมากนัก นอกจากนี้ แม้ว่าในอุสาหกรรมเกมจะมีผู้เล่นอยู่เป็นจำนวนมาก แต่ก็ยังไม่มีเกมหรือแพลตฟอร์มใดที่ให้สิทธิผู้เล่นในการเป็นเจ้าของสินทรัพย์หรือข้อมูลภายในเกมอย่างแท้จริง โดยแม้จะมีความพยายามในการระดมทุนเพื่อทำเกมหรือแพลตฟอร์มที่ให้ผู้เล่นได้มีโอกาสเป็นเจ้าของสินทรัพย์หรือข้อมูลภายในเกมอยู่บ้าง แต่ก็ยังคงอยู่ในขั้นตอนการทดลองที่ยังไม่เสร็จสมบูรณ์ และผู้เล่นยังต้องพึ่งพาระบบนิเวศน์ของแพลตฟอร์มนั้น ๆ อีกด้วย ในวิทยานิพนธ์ฉบับนี้ ผู้วิจัยจึงประสงค์ที่จะนำเสนอสถาปัตยกรรมกลางที่ทำให้ผู้เล่นเกมสามารถเป็นเจ้าของเวลาที่ตนเองใช้ภายในเกมได้โดยใช้บล็อกเชนสาธารณะ ทั้งผู้เล่นยังสามารถนำเวลาดังกล่าวไปใช้ในเกมอื่นได้ด้วย โดยใช้มาตราฐานโทเคนดิจิทัล ERC-20 บนอีเธอเรี่ยม นอกจากนี้ รูปแบบสถาปัตยกรรมที่นำเสนอดังกล่าวยังสามารถประยุกต์ใช้ได้กับทุกบล็อกเชนสาธารณะ และยังเป็นประโยชน์ต่อทุกองค์ประกอบของระบบนิเวศน์ อาทิเช่น ผู้เล่น บล็อกเชนโหนด และผู้พัฒนาเกม โดยผลการทดลองในงานวิทยานิพนธ์นี้ ยังแสดงว่าแนวความคิดดังกล่าวทำให้ผู้เล่นใช้เวลาในการเล่นเกมนานขึ้น และมีแนวโน้มที่จะอยากเล่นเกมใหม่ๆ ที่สามารถนำมูลค่าในเกมเดิมไปใช้ได้ แต่ทั้งนี้ยังมีปัจจัยหลายอย่างที่มีผล อาทิเช่น ประเภทของเกม การแลกเปลี่ยนค่าของเวลาภายในเกม เป็นต้น

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Thai Scene Text Recognition, Thananop Kobchaisawat Jan 2019

Thai Scene Text Recognition, Thananop Kobchaisawat

Chulalongkorn University Theses and Dissertations (Chula ETD)

Automatic scene text detection and recognition can benefit a large number of daily life applications such as reading signs and labels, and helping visually impaired persons. Reading scene text images becomes more challenging than reading scanned documents in many aspects due to many factors such as variations of font styles and unpredictable lighting conditions. The problem can be decomposed into two sub-problems: text localization and text recognition. The proposed scene text localization works at the pixel level combined with a new text representation and a fully-convolutional neural network. This method is capable of detecting arbitrary shape texts without language limitations. …

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The Evaluation Of An Android Permission Management System Based On Crowdsourcing, Pulkit Rustgi Jan 2019

The Evaluation Of An Android Permission Management System Based On Crowdsourcing, Pulkit Rustgi

Theses and Dissertations

Mobile and web application security, particularly concerning the area of data privacy, has received much attention from the public in recent years. Most applications are installed without disclosing full information to users and clearly stating what they have access to. This often raises concerns when users become aware of unnecessary information being collected or stored. Unfortunately, most users have little to no technical knowledge in regard to what permissions should be granted and can only rely on their intuition and past experiences to make relatively uninformed decisions. DroidNet, a crowdsource based Android recommendation tool and framework, is a proposed avenue …

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Optimal Rescheduling Of Real Power To Mitigate Congestion Using Gravitational Search Algorithm, Kaushik Paul, Niranjan Kumar, Shaligram Agrawal, Kamalendu Paul Jan 2019

Optimal Rescheduling Of Real Power To Mitigate Congestion Using Gravitational Search Algorithm, Kaushik Paul, Niranjan Kumar, Shaligram Agrawal, Kamalendu Paul

Turkish Journal of Electrical Engineering and Computer Sciences

The initiative to manage congestion has gained interest in the current deregulated scenario. The principle commitment of the work in this article is to extend the gravitational search algorithm (GSA) as an efficient metaheuristic optimizing algorithm to diminish the rescheduling cost and efficiently attenuate the overloading of the line with the minimal deviation in the active power generation. The congestion management drive is accomplished by prioritizing the generators based on their sensitivity values. Thereafter, the GSA is introduced to optimally minimize the rescheduling cost along with the minimization of the total amount of active power output and system losses. The …

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A New Approach For Parameter Estimation Of The Single-Diode Model Forphotovoltaic Cells/Modules, Bi̇lge Kaan Atay, Ulaş Emi̇noğlu Jan 2019

A New Approach For Parameter Estimation Of The Single-Diode Model Forphotovoltaic Cells/Modules, Bi̇lge Kaan Atay, Ulaş Emi̇noğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Solar energy has become a popular renewable energy source, leading to wide use of photovoltaic (PV) cells/modules in energy production. For this reason, realistic modeling of PVs and determining the equivalent circuit parameters is of great importance in terms of planning and operation. Hence, in this study, an analytical model for identifying the single-diode equivalent circuit parameters; series resistance (Rs ), shunt resistance (Rp ), diode ideality factor (a), diode reverse-saturation current (Io ), and photon current (Ipv ) for PV cells/modules is developed without neglecting any term. In order to test the accuracy of the model, a number of …

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Lower Order Controller Design Using Weighted Singular Perturbation Approximation, Muhammad Raees Furquan Azhar, Umair Zulfiqar, Muwahida Liaquat Jan 2019

Lower Order Controller Design Using Weighted Singular Perturbation Approximation, Muhammad Raees Furquan Azhar, Umair Zulfiqar, Muwahida Liaquat

Turkish Journal of Electrical Engineering and Computer Sciences

Most of the analytical design procedures yield controllers of almost the same order as that of the plant. Resultantly, if the plant is of a high order, the controller obtained from these design procedures is also of a high order. The order of the controller should be practically acceptable for easy implementation. There are two indirect methods for designing a low order controller for high order plants: plant reduction and compensator reduction. In compensator reduction, the order of the controller designed for the original higher order plant is reduced. In plant reduction, the order of the plant is reduced for …

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Enabling Space Time Division Multiple Access In Ietf 6tisch Protocol, Sedat Görmüş, Sercan Külcü Jan 2019

Enabling Space Time Division Multiple Access In Ietf 6tisch Protocol, Sedat Görmüş, Sercan Külcü

Turkish Journal of Electrical Engineering and Computer Sciences

IETF 6TiSCH standard aims to create reliable, deterministic, and low-power networks by scheduling bandwidth resources in time and frequency domains. The main emphasis of 6TiSCH protocol is that it creates Internet of things (IoT) networks with a deterministic and controllable delay. However, many of its benefits are tied to the ability of the 6TiSCH scheduler to optimally distribute radio resources among wireless nodes which may not be possible when the number of frequency resources are limited and several other wireless technologies share the same frequency band (e.g., WiFi, Bluetooth and IEEE 802.15.4). Here the integration of a low-complexity directional antenna …

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Stegogis: A New Steganography Method Using The Geospatial Domain, Ömer Kurtuldu, Mehmet Demi̇rci̇ Jan 2019

Stegogis: A New Steganography Method Using The Geospatial Domain, Ömer Kurtuldu, Mehmet Demi̇rci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Geographic data are used on a variety of computing devices for many different applications including navigation, tracking, location planning, and marketing. The prevalence of geographic data makes it possible to envision new useful applications. In this paper, we propose using geographic data as a medium for secret communication, or steganography. We develop a method called StegoGIS for hiding messages in geographic coordinates in the well-known binary of fast-moving objects and transmitting them secretly. We show that discovering this secret communication is practically impossible for third parties. We also show that a large amount of secret data can be transmitted this …

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Improving Undersampling-Based Ensemble With Rotation Forest For Imbalanced Problem, Huaping Guo, Xiaoyu Diao, Hongbing Liu Jan 2019

Improving Undersampling-Based Ensemble With Rotation Forest For Imbalanced Problem, Huaping Guo, Xiaoyu Diao, Hongbing Liu

Turkish Journal of Electrical Engineering and Computer Sciences

As one of the most challenging and attractive issues in pattern recognition and machine learning, the imbalanced problem has attracted increasing attention. For two-class data, imbalanced data are characterized by the size of one class (majority class) being much larger than that of the other class (minority class), which makes the constructed models focus more on the majority class and ignore or even misclassify the examples of the minority class. The undersampling-based ensemble, which learns individual classifiers from undersampled balanced data, is an effective method to cope with the class-imbalance data. The problem in this method is that the size …

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Examination Of Adoption Theory On The Devops Practice Of Continuous Delivery, Andrew John Anderson Jan 2019

Examination Of Adoption Theory On The Devops Practice Of Continuous Delivery, Andrew John Anderson

Walden Dissertations and Doctoral Studies

Many organizations have difficulty adopting advanced software development practices. Some software development project managers in large organizations are not aligned with the relationship between performance expectancy, effort expectancy, social influence, and facilitating conditions, as moderated by experience, with intent to adopt the DevOps practice of continuous delivery. The purpose of this study was to examine the statistical relationships between the independent variables—performance expectancy, effort expectancy, social influence, and facilitating conditions, as moderated by experience—and the dependent variable of behavioral intent to adopt a continuous delivery system. Venkatesh, Morris, Davis, and Davis's unified theory of acceptance and use of technology provided …

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Evaluation Of Suas Education And Training Tools, Brent Terwilliger, Scott Burgess, James Solti, Kristine Kiernan, Christian Janke, Andrew Shepherd Jan 2019

Evaluation Of Suas Education And Training Tools, Brent Terwilliger, Scott Burgess, James Solti, Kristine Kiernan, Christian Janke, Andrew Shepherd

Publications

The wide distribution and demographic composition of students seeking small unmanned aircraft system (sUAS) education presents a need to fully understand the capabilities, limitations, and dependencies of effective training tools. Concepts, practices, and technologies associated with modeling and simulation, immersive gaming, augmented and mixed-reality, and remote operation have demonstrated efficacy to support engaged student learning and objective satisfaction. Identification and comparison of key attributes critical to an aviation educational framework, such as competency-based training, enables educational designers to identify those tools with the highest potential to support successful learning. A series of factors, such as system performance, regulatory compliance, environmental …

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Noise Reduction In Eeg Signals Using Convolutional Autoencoding Techniques, Conor Hanrahan Jan 2019

Noise Reduction In Eeg Signals Using Convolutional Autoencoding Techniques, Conor Hanrahan

Dissertations

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with …

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Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi Jan 2019

Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi

Engineering Faculty Articles and Research

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM)— find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust …

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Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai Jan 2019

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

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Development Of Semantic Scene Conversion Model For Image-Based Localization At Night, Dongyoun Kim Jan 2019

Development Of Semantic Scene Conversion Model For Image-Based Localization At Night, Dongyoun Kim

Electronic Theses and Dissertations

Developing an autonomous vehicle navigation system invariant to illumination change is one of the biggest challenges in vision-based localization field due to the fact that the appearance of an image becomes inconsistent under different light conditions even with the same location. In particular, the night scene images have greatest change in appearance compared to the according day scenes. Moreover, the night images do not have enough information in Image-based localization. To deal with illumination change, image conversion methods have been researched. However, these methods could lose the detail of objects and add fake objects into the output images. In this …

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Edge Heterogeneous Hardware Evaluation Based On Real Connected And Autonomous Vehicles (Cavs) Workloads, Mustafa Ahmad Jan 2019

Edge Heterogeneous Hardware Evaluation Based On Real Connected And Autonomous Vehicles (Cavs) Workloads, Mustafa Ahmad

Research Opportunities for Engineering Undergraduates (ROEU) Program 2018-19

There has recently been a wide expansion of hardware to assist in autonomous driving tasks. On this project, we focus on using some state-of-the-art deep learning workloads in connected autonomous vehicle (CAV) scenarios,such as object detection and object tracking to evaluate the heterogeneous hardware.

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Pv-Based Off-Board Electric Vehicle Battery Charger Using Bidc, Ankita Paul, Krithiga Subramanian, Sujitha N Jan 2019

Pv-Based Off-Board Electric Vehicle Battery Charger Using Bidc, Ankita Paul, Krithiga Subramanian, Sujitha N

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the use of renewable energy sources is increasing drastically in several sectors, which leads to its role in the automobile industry to charge electric vehicle (EV) batteries. In this paper, a photovoltaic (PV) array-fed off-board battery charging system using a bidirectional interleaved DC-DC converter (BIDC) is proposed for light-weight EVs. This off-board charging system is capable of operating in dual mode, thereby supplying power to the EV battery from the PV array in standstill conditions and driving the DC load by the EV battery during running conditions. This dual mode operation is accomplished by the use of …

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A Control Scheme For Maximizing The Delivered Power To The Load In A Standalonewind Energy Conversion System, Saeed Heshmatian, Davood A. Khaburi, Mahyar Khosravi, Ahad Kazemi Jan 2019

A Control Scheme For Maximizing The Delivered Power To The Load In A Standalonewind Energy Conversion System, Saeed Heshmatian, Davood A. Khaburi, Mahyar Khosravi, Ahad Kazemi

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a control scheme is proposed for maximum power point tracking (MPPT) in a variable speed standalone wind energy conversion system (WECS) with permanent magnet synchronous generator. A MPPT algorithm is designed trying to eliminate the main deficiency of the conventional perturbation and observation (P&O) method, which is the challenge of choosing a proper step size and the unwanted trade-off between accuracy and speed. The designed algorithm properly addresses this drawback and significantly improves the MPPT performance. Another important issue is to ensure fast and accurate tracking of the optimal reference point obtained from the MPPT algorithm and …

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Performance Enhancement Of Photovoltaic System Using Genetic Algorithm- Based Maximum Power Point Tracking, Brammanayagam Nagarani, Jothiswaroopan Nesamony Jan 2019

Performance Enhancement Of Photovoltaic System Using Genetic Algorithm- Based Maximum Power Point Tracking, Brammanayagam Nagarani, Jothiswaroopan Nesamony

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, enormous progress has been made on power generation using photovoltaic (PV) system. Solar power is one of the most promising renewable energy sources that is providing its benefit specifically in rural areas. With the increasing need for solar energy, it becomes necessary to extract maximum power from the PV array. The output power of the solar cells varies directly with the ambient temperature and Irradiation. Therefore, the challenge is to track maximum power from the PV array when environmental factors change. This paper focuses on increasing the efficiency of a PV array by incorporating artificial intelligence techniques. …

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Exploring Bigram Character Features For Arabic Text Clustering, Dia Eddin Abuzeina Jan 2019

Exploring Bigram Character Features For Arabic Text Clustering, Dia Eddin Abuzeina

Turkish Journal of Electrical Engineering and Computer Sciences

The vector space model (VSM) is an algebraic model that is widely used for data representation in text mining applications. However, the VSM poses a critical challenge, as it requires a high-dimensional feature space. Therefore, many feature selection techniques, such as employing roots or stems (i.e. words without infixes and prefixes, and/or suffixes) instead of using complete word forms, are proposed to tackle this space challenge problem. Recently, the literature shows that one more basic unit feature can be used to handle the textual features, which is the twoneighboring character form that we call microword. To evaluate this feature type, …

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Design Of A Substrate Integrated Waveguide Matrix Amplifier, Shabnam Ahamadi Andevari, Gholamreza Moradi Jan 2019

Design Of A Substrate Integrated Waveguide Matrix Amplifier, Shabnam Ahamadi Andevari, Gholamreza Moradi

Turkish Journal of Electrical Engineering and Computer Sciences

Developments in microwave systems have increased the need for matrix amplifiers, which provide both high gain and wide frequency bands. The aim of this paper is to design a novel 2x4 matrix amplifier with a substrate integrated waveguide (SIW)-based power divider and combiner and a microstrip gain equalizer in the X and low Ku frequency bands. The proposed amplifier can be easily integrated with any microstrip, rectangular waveguide, or SIW-based circuits. The analysis and design of the amplifier is performed using two full wave simulators with different computational techniques (finite element method and finite integration technique) to verify the results. …

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Symptom-Aware Hybrid Fault Diagnosis Algorithm In The Network Virtualization Environment, Yuze Su, Xiangru Meng, Xiaoyang Han, Qiaoyan Kang Jan 2019

Symptom-Aware Hybrid Fault Diagnosis Algorithm In The Network Virtualization Environment, Yuze Su, Xiangru Meng, Xiaoyang Han, Qiaoyan Kang

Turkish Journal of Electrical Engineering and Computer Sciences

As an important technology in next-generation networks, network virtualization has received more and more attention. Fault diagnosis is the crucial element for fault management and it is the process of inferring the exact failure in the network virtualization environment (NVE) from the set of observed symptoms. Although various traditional fault diagnosis algorithms have been proposed, the virtual network has some new characteristics, which include inaccessible fault information of the substrate network, inaccurate network observations, and a dynamic embedding relationship. To solve these challenges, a symptom-aware hybrid fault diagnosis (SAHFD) algorithm in the NVE is proposed in this paper. First, a …

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Community Detection In Complex Networks Using A New Agglomerative Approach, Majid Arasteh, Somayeh Alizadeh Jan 2019

Community Detection In Complex Networks Using A New Agglomerative Approach, Majid Arasteh, Somayeh Alizadeh

Turkish Journal of Electrical Engineering and Computer Sciences

Complex networks are used for the representation of complex systems such as social networks. Graph analysis comprises various tools such as community detection algorithms to uncover hidden data. Community detection aims to detect similar subgroups of networks that have tight interconnections with each other while, there is a sparse connection among different subgroups. In this paper, a greedy and agglomerative approach is proposed to detect communities. The proposed method is fast and often detects high-quality communities. The suggested method has several steps. In the first step, each node is assigned to a separated community. In the second step, a vertex …

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A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George Jan 2019

A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a new ensemble feature selector, called the majority voting feature selector (MVFS), for developing new maximal oxygen uptake (VO2max) prediction models using a support vector machine (SVM). The approach is based on rank aggregation, which meaningfully utilizes the correlation among the relevance ranks of predictor variables given by three state-of-the-art feature selectors: Relief-F, minimum redundancy maximum relevance (mRMR), and maximum likelihood feature selection (MLFS). By applying the SVM combined with MVFS on a self-created dataset containing maximal and submaximal exercise data from 185 college students, several new hybrid (VO2max) prediction models have been created. To compare the …

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