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Articles 4051 - 4080 of 6321
Full-Text Articles in Entire DC Network
Gait Analysis From Wearable Devices Using Image And Signal Processing, Bradley A. Schneider
Gait Analysis From Wearable Devices Using Image And Signal Processing, Bradley A. Schneider
Browse all Theses and Dissertations
We present the results of analyzing gait motion in-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera and are validated using the ground truth dataset. We perform an additional data collection with both the camera and a body-worn accelerometer …
Comparison Of Two-Pass Algorithms For Dynamic Topic Modelling Based On Matrix Decompositions, John Cardiff, Gabriella Skitalinskaya, Mikhail Alexandrov
Comparison Of Two-Pass Algorithms For Dynamic Topic Modelling Based On Matrix Decompositions, John Cardiff, Gabriella Skitalinskaya, Mikhail Alexandrov
Conference Papers
In this paper we present a two-pass algorithm based on different matrix decompositions, such as LSI, PCA, ICA and NMF, which allows tracking of the evolution of topics over time. The proposed dynamic topic models as output give an easily interpreted overview of topics found in a sequentially organized set of documents that does not require further processing. Each topic is presented by a user-specified number of top-terms. Such an approach to topic modeling if applied to, for example, a news article data set, can be convenient and useful for economists, sociologists, political scientists. The proposed approach allows to achieve …
Semi-Supervised Approach To Monitoring Clinical Depressive Symptoms In Social Media, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth
Semi-Supervised Approach To Monitoring Clinical Depressive Symptoms In Social Media, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth
Computer Science and Engineering Faculty Publications
With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms …
Relatedness-Based Multi-Entity Summarization, Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng
Relatedness-Based Multi-Entity Summarization, Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng
Kno.e.sis Publications
Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and …
Developing Mental Rotation Ability Through Engagement In Assignments That Involve Solids Of Revolution, Atara Shriki, Ruthi Barkai, Dorit Patkin
Developing Mental Rotation Ability Through Engagement In Assignments That Involve Solids Of Revolution, Atara Shriki, Ruthi Barkai, Dorit Patkin
The Mathematics Enthusiast
Spatial ability is essential for succeeding in the STEM (Sciences, Technology, Engineering and Mathematics) disciplines, especially mental rotation. Research points out that spatial ability is malleable, and therefore calls for developing learners’ ability by engaging them in appropriate assignments, starting from kindergarten. Given this, our paper presents several assignments designed for mathematics prospective teachers with the aim of fostering their mental rotation skills. Specifically, these assignments deal with solids of revolution, three-dimensional shapes formed by revolving a planar shape about a given axis that lies on the same plane.
The Document Similarity Network: A Novel Technique For Visualizing Relationships In Text Corpora, Dylan Baker
The Document Similarity Network: A Novel Technique For Visualizing Relationships In Text Corpora, Dylan Baker
HMC Senior Theses
With the abundance of written information available online, it is useful to be able to automatically synthesize and extract meaningful information from text corpora. We present a unique method for visualizing relationships between documents in a text corpus. By using Latent Dirichlet Allocation to extract topics from the corpus, we create a graph whose nodes represent individual documents and whose edge weights indicate the distance between topic distributions in documents. These edge lengths are then scaled using multidimensional scaling techniques, such that more similar documents are clustered together. Applying this method to several datasets, we demonstrate that these graphs are …
Machine Learning And Natural Language Methods For Detecting Psychopathy In Textual Data, Andrew Stephen Henning
Machine Learning And Natural Language Methods For Detecting Psychopathy In Textual Data, Andrew Stephen Henning
Electronic Theses and Dissertations
Among the myriad of mental conditions permeating through society, psychopathy is perhaps the most elusive to diagnose and treat. With the advent of natural language processing and machine learning, however, we have ushered in a new age of technology that provides a fresh toolkit for analyzing text and context. Because text remains the medium of choice for most personal and professional interactions, it may be possible to use textual samples from psychopaths as a means for understanding and ultimately classifying similar individuals based on the content of their language usage. This paper aims to investigate natural language processing and supervised …
A Study Of Fourth-Grade Students' Perceptions On Homework Environment And Academic Motivation In Mathematics, Stefanie Harmon
A Study Of Fourth-Grade Students' Perceptions On Homework Environment And Academic Motivation In Mathematics, Stefanie Harmon
Walden Dissertations and Doctoral Studies
The problem at an elementary school is teachers' lack of knowledge and information on the perceptions and motivation of students to complete independent mathematics homework. The purpose of this study was to identify students' perceptions regarding their homework environment and academic motivation in mathematics. The study's conceptual framework, attribution theory, supported the examination of drivers of motivation for participants related to homework completion. Guiding research questions, supported by Keller's ARCS model, focused on the identification of students' perceptions of homework attention, relevance, curiosity, satisfaction, and their preferred homework environment. This qualitative research study obtained data from semistructured interviews with 44 …
Temporal Alignment Using The Incremental Unit Framework, Casey Kennington, Ting Han, David Schlangen
Temporal Alignment Using The Incremental Unit Framework, Casey Kennington, Ting Han, David Schlangen
Computer Science Faculty Publications and Presentations
We propose a method for temporal alignments--a precondition of meaningful fusions--of multimodal systems, using the incremental unit dialogue system framework, which gives the system flexibility in how it handles alignment: either by delaying a modality for a specified amount of time, or by revoking (i.e., backtracking) processed information so multiple information sources can be processed jointly. We evaluate our approach in an offline experiment with multimodal data and find that using the incremental framework is flexible and shows promise as a solution to the problem of temporal alignment in multimodal systems.
An Alternative Approach To Training Sequence-To-Sequence Model For Machine Translation, Vivek Sah
An Alternative Approach To Training Sequence-To-Sequence Model For Machine Translation, Vivek Sah
Honors Theses
Machine translation is a widely researched topic in the field of Natural Language Processing and most recently, neural network models have been shown to be very effective at this task. The model, called sequence-to-sequence model, learns to map an input sequence in one language to a vector of fixed dimensionality and then map that vector to an output sequence in another language without any human intervention provided that there is enough training data. Focusing on English-French translation, in this paper, I present a way to simplify the learning process by replacing English input sentences by word-by-word translation of those sentences. …
Smart Image Search System Using Personalized Semantic Search Method, Fangyu Zhang
Smart Image Search System Using Personalized Semantic Search Method, Fangyu Zhang
Electronic Theses and Dissertations
Due to the emerge in huge numbers of information on the internet nowadays, search technologies are widely used in various fields. Achieving the most relevant search result for the users becomes a big challenge now. While the traditional semantic search technologies seem to achieve the most relevant search result, however, it faces two problems: one is the one-size-fits-all problem, and another is low efficiency. The purpose of this research is to build a Smart Image Search System by using the personalized semantic search method to solve those problems. The personalized semantic search method makes the search system avoids the one-size-fits-all …
Language Learning Under Classroom Conditions During The Transition To Hybrid Instruction: A Case-Study Of Student Performance During The Implementation Of Instructional Technology, Lisbeth O. Swain, Timothy D. Swain
Language Learning Under Classroom Conditions During The Transition To Hybrid Instruction: A Case-Study Of Student Performance During The Implementation Of Instructional Technology, Lisbeth O. Swain, Timothy D. Swain
Marine & Environmental Sciences Faculty Articles
We examined the unmanipulated performance of students under real classroom conditions in order to assess the effect of a technology-enhanced hybrid learning approach to second language, (L2) instruction on beginning and advanced Spanish language learners. This research focused on the transition period of technology implementation when the entire section of Spanish of a modern language department of a liberal arts university transitioned from traditional face-to-face instruction, to a technology-enhanced hybrid learning approach with concurrent reduction in face-to-face classroom hours. This implementation provided an opportunity to compare the performance of students prior to and during the transition to hybrid model of …
Automatic Idiom Recognition With Word Embeddings, Jing Peng, Anna Feldman
Automatic Idiom Recognition With Word Embeddings, Jing Peng, Anna Feldman
Department of Computer Science Faculty Scholarship and Creative Works
Expressions, such as add fuel to the fire, can be interpreted literally or idiomatically depending on the context they occur in. Many Natural Language Processing applications could improve their performance if idiom recognition were improved. Our approach is based on the idea that idioms and their literal counterparts do not appear in the same contexts. We propose two approaches: (1) Compute inner product of context word vectors with the vector representing a target expression. Since literal vectors predict well local contexts, their inner product with contexts should be larger than idiomatic ones, thereby telling apart literals from idioms; and (2) …
Presenting A Labelled Dataset For Real-Time Detection Of Abusive User Posts, Hao Chen, Susan Mckeever, Sarah Jane Delany
Presenting A Labelled Dataset For Real-Time Detection Of Abusive User Posts, Hao Chen, Susan Mckeever, Sarah Jane Delany
Conference papers
Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, …
Extended List Of Stop Words: Does It Work For Keyphrase Extraction From Short Texts?, Svetlana Popova, Gabriella Skitalinskaya
Extended List Of Stop Words: Does It Work For Keyphrase Extraction From Short Texts?, Svetlana Popova, Gabriella Skitalinskaya
Conference Papers
In this paper we study the problem of key phrase extraction from short texts written in Russian. As texts we consider messages posted on Internet car forums related to the purchase or repair of cars. The main assumption made is: the construction of lists of stop words for key phrase extraction can be effective if performed on the basis of a small, expert-marked collection. The results show that even a small number of texts marked by an expert can be enough to build an extended list of stop words. Extracted stop words allow to improve the quality of the key …
A Neuro-Fuzzy Controller For Grid-Connected Heavy-Duty Gas Turbine Power Plants, Mohamed Iqbal Mohamed Mustafa, Joseph Xavier Rayappan, Kanakaraj Jagannathan
A Neuro-Fuzzy Controller For Grid-Connected Heavy-Duty Gas Turbine Power Plants, Mohamed Iqbal Mohamed Mustafa, Joseph Xavier Rayappan, Kanakaraj Jagannathan
Turkish Journal of Electrical Engineering and Computer Sciences
Frequent load fluctuation and set-point variation may affect the stability of grid-connected heavy-duty gas turbine power plants. To overcome such problems, a novel neuro-fuzzy controller is proposed in this paper for single-shaft heavy-duty gas turbines ranging from 18.2 MW to 106.7 MW. A neuro-fuzzy controller was developed using a hybrid learning algorithm and the effectiveness of the controller for all heavy-duty gas turbine plants (5, 6, 7, and 9 series) is demonstrated against load disturbance and set-point variation in a grid-connected environment. Various time domain specifications and performance index criteria of the neuro-fuzzy controller are compared with that of a …
Nonlinear Model Predictive Control Based On Fuzzy Wavelet Neural Network And Chaos Optimization, Mehrnoush Davanipour, Hanieh Asadipooya
Nonlinear Model Predictive Control Based On Fuzzy Wavelet Neural Network And Chaos Optimization, Mehrnoush Davanipour, Hanieh Asadipooya
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper a combined controller is proposed for nonlinear dynamical systems. The controller is constructed by a fuzzy wavelet network and nonlinear model predictive control. Chaotic optimization, which is fast and robust, is applied to generate optimized controlled input in nonlinear model predictive control. The ability of the fuzzy wavelet neural network and the proposed controller is shown by simulation. It is illustrated that the proposed method is able to increase the speed of tracking in addition to having very little steady state error. Using chaotic optimization makes the controller robust in the presence of noise.
Complexity Theoretic Parallels Among Automata, Formal Languages And Real Variables : Including Multi-Patterns, L-Systems And Cellular Automata, Jingnan Xie
Legacy Theses & Dissertations (2009 - 2024)
In this dissertation, we emphasize productiveness not just undecidability since pro- ductiveness implies constructive incompleteness. Analogues of Rice’s Theorem for different classes of languages are investigated, refined and generalized. In particular, several sufficient but general conditions are presented for predicates to be as hard as some widely discussed predicates such as “= ∅” and “= {0,1}∗”. These conditions provide several general methods for proving complexity/productiveness results and apply to a large number of simple and natural predicates. As the first step in apply- ing these general methods, we investigate the complexity/productiveness of the pred- icates “= ∅”, “= {0,1}∗” and …
Depositional Stages Of The Eğribucak Inner Basin (Terrestrial To Marine Evaporite And Carbonate) From The Sivas Basin (Central Anatolia, Turkey), Özgen Kangal, Nazi̇re Erdem, Baki̇ Erdoğan Varol
Depositional Stages Of The Eğribucak Inner Basin (Terrestrial To Marine Evaporite And Carbonate) From The Sivas Basin (Central Anatolia, Turkey), Özgen Kangal, Nazi̇re Erdem, Baki̇ Erdoğan Varol
Turkish Journal of Earth Sciences
The Sivas Cenozoic Basin and coeval Central Anatolian basins such as Çankırı and Tuz Gölü are characterized by both marine and terrestrial sediments ranging in age from the Eocene to early Miocene. The evaporite regime here generally appeared during the late stage of Eocene transgression and persisted through the Oligocene time. However, marine-induced Oligocene evaporites are less known because of less paleontological evidence and regional tectonics and salt diapirism that mostly caused the destruction of their original stratigraphic positions. The Eğribucak area studied here, located about 25 km southeast of Sivas, provides a well-stratified key section to shed light on …
A New Word-Based Compression Model Allowing Compressed Pattern Matching, Hali̇l Nusret Buluş, Aydin Carus, Altan Mesut
A New Word-Based Compression Model Allowing Compressed Pattern Matching, Hali̇l Nusret Buluş, Aydin Carus, Altan Mesut
Turkish Journal of Electrical Engineering and Computer Sciences
In this study a new semistatic data compression model that has a fast coding process and that allows compressed pattern matching is introduced. The name of the proposed model is chosen as tagged word-based compression algorithm (TWBCA) since it has a word-based coding and word-based compressed matching algorithm. The model has two phases. In the first phase a dictionary is constructed by adding a phrase, paying attention to word boundaries, and in the second phase compression is done by using codewords of phrases in this dictionary. The first byte of the codeword determines whether the word is compressed or not. …
Multiverse Optimized Fuzzy-Pid Controller With A Derivative Filter For Load Frequency Control Of Multisource Hydrothermal Power System, Amit Kumar, Sathans Suhag
Multiverse Optimized Fuzzy-Pid Controller With A Derivative Filter For Load Frequency Control Of Multisource Hydrothermal Power System, Amit Kumar, Sathans Suhag
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a multiverse optimized (MVO) fuzzy PID controller with a derivative filter (fuzzy-PIDF) is proposed for the load frequency control (LFC) of a two-area multisource hydrothermal power system. The superiority of the MVO algorithm is demonstrated by comparing the system LFC performance with integral and fuzzy-PIDF controllers, both optimized using MVO, as well as some of the recent heuristic optimization techniques such as the ant lion optimizer, gray wolf optimizer, differential evolution, bacterial foraging optimization algorithm, and particle swarm optimization. To the best of the knowledge of the authors, the use of the MVO technique has not yet …
Indirect Adaptive Neurofuzzy Hermite Wavelet Based Control Of Pv In A Grid-Connected Hybrid Power System, Sidra Mumtaz, Laiq Khan
Indirect Adaptive Neurofuzzy Hermite Wavelet Based Control Of Pv In A Grid-Connected Hybrid Power System, Sidra Mumtaz, Laiq Khan
Turkish Journal of Electrical Engineering and Computer Sciences
Owing to the evolution of the smart grid, the emergence of hybrid power systems (HPSs), and the proliferation of plug-in-hybrid electric vehicles, the development of efficient and robust maximum power point tracking (MPPT) algorithms for renewable energy sources due to their inherent intermittent nature has overwhelmed the power industry. In this paper, an incremental conductance (IC) based Hermite wavelet incorporated neurofuzzy indirect adaptive MPPT control paradigm for a photovoltaic (PV) system in a grid-connected HPS is proposed. The performance of the proposed adaptive Hermite wavelet incorporated neurofuzzy MPPT control paradigm is validated through a comprehensive grid-connected HPS test-bed developed in …
Turkish Synonym Identification From Multiple Resources: Monolingual Corpus, Mono/Bilingual Online Dictionaries, And Wordnet, Tuğba Yildiz, Banu Di̇ri̇, Savaş Yildirim
Turkish Synonym Identification From Multiple Resources: Monolingual Corpus, Mono/Bilingual Online Dictionaries, And Wordnet, Tuğba Yildiz, Banu Di̇ri̇, Savaş Yildirim
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a model is proposed to determine synonymy by incorporating several resources. The model extracts the features from monolingual online dictionaries, a bilingual online dictionary, WordNet, and a monolingual Turkish corpus. Once it has built a candidate list, it determines the synonymy for a given word by means of those features. All these resources and the approaches are evaluated. Taking all features into account and applying machine learning algorithms, the model shows good performance of F-measure with 81.4%. The study contributes to the literature by integrating several resources and attempting the first corpus-driven synonym detection system for Turkish.
A Self-Tuning Neurofuzzy Feedback Linearization-Based Damping Control Strategy For Multiple Hvdc Links, Saghir Ahmad, Laiq Khan
A Self-Tuning Neurofuzzy Feedback Linearization-Based Damping Control Strategy For Multiple Hvdc Links, Saghir Ahmad, Laiq Khan
Turkish Journal of Electrical Engineering and Computer Sciences
This research work proposes a multi-input multi-output (MIMO) online adaptive feedback linearization NeuroFuzzy control (AFLNFC) scheme to improve the damping of low frequency oscillations (LFOs) in an AC/DC power system. Optimized NeuroFuzzy identification architecture online captures the oscillatory dynamics of the power system through wide area measurement system (WAMS)-based measured speed signals of machines. Based on the identified power system model, the appropriate control law is derived through feedback linearization control with a self-tuned coefficient vector. The generated control signal modulates the real power flow through a high voltage direct current (HVDC) link during perturbed operating conditions and enhances system …
A Data-Aware Cognitive Engine For Scheduling Data Intensive Applications In A Grid, Vijaya Nagarajan, Maluk Mohamed Mulk Abdul
A Data-Aware Cognitive Engine For Scheduling Data Intensive Applications In A Grid, Vijaya Nagarajan, Maluk Mohamed Mulk Abdul
Turkish Journal of Electrical Engineering and Computer Sciences
Data-intensive applications produce huge amounts of data that need to be stored, analyzed, and interpreted. A data grid serves as a cost-effective infrastructure for solving these data-intensive applications. Existing scheduling strategies are best suited for handling compute-intensive applications, although they lack in performance while handling data-intensive applications. In this work, a novel mechanism of incorporating cognitive science in a data grid is proposed for scheduling data-intensive workflows. A unique model is derived in which a cognitive engine (CE) is built into the middleware of the data grid. The intelligent agents present in the CE handle the request for data sets …
Adslanf: A Negotiation Framework For Cloud Management Systems Using A Bulk Negotiation Behavioral Learning Approach, Rajkumar Rajavel, Mala Thangarathinam
Adslanf: A Negotiation Framework For Cloud Management Systems Using A Bulk Negotiation Behavioral Learning Approach, Rajkumar Rajavel, Mala Thangarathinam
Turkish Journal of Electrical Engineering and Computer Sciences
One of the major challenges in cloud computing is the development of a service-level agreement (SLA) negotiation framework using an intelligent third-party broker negotiation strategy. Current frameworks exploit various negotiation strategies using game theoretic, heuristic, and argumentation-based approaches for obtaining optimal negotiation with a better success rate (negotiation commitment). However, these approaches fail to optimize the negotiation round (NR), total negotiation time (TNT), and communication overhead (CO) involved in the negotiation strategy. To overcome these problems, certain researchers have exploited trade-off, concession, and behavioral learning strategies with varying degrees of sacrifices (reductions) in their concerned proposal generation. Such sacrifices can …
A Hybrid Of Sliding Mode Control And Fuzzy Logic Control Using A Fuzzy Supervisory Switched System For Dc Motor Speed Control, Husain Ahmed, Abha Rajoriya
A Hybrid Of Sliding Mode Control And Fuzzy Logic Control Using A Fuzzy Supervisory Switched System For Dc Motor Speed Control, Husain Ahmed, Abha Rajoriya
Turkish Journal of Electrical Engineering and Computer Sciences
In many applications of DC motor speed control systems, PID controllers are mostly used. Such control schemes do not show good performance when input and load torque disturbances are applied. This paper shows that sliding mode (SM) can give better control due to its robust property but it has an unwanted chattering problem associated with it, which is harmful to the plant. To limit this problem a hybrid controller is presented in which a fuzzy controller is combined with the SM controller by a fuzzy supervisory system in such a way that the SM controller works in transient state and …
Android Malware Classification Based On Anfis With Fuzzy C-Means Clustering Using Significant Application Permissions, Altyeb Altaher, Omar Barukab
Android Malware Classification Based On Anfis With Fuzzy C-Means Clustering Using Significant Application Permissions, Altyeb Altaher, Omar Barukab
Turkish Journal of Electrical Engineering and Computer Sciences
Mobile phones have become an essential part of our lives because we depend on them to perform many tasks, and they contain personal and important information. The continuous growth in the number of Android mobile applications resulted in an increase in the number of malware applications, which are real threats and can cause great losses. There is an urgent need for efficient and effective Android malware detection techniques. In this paper, we present an adaptive neuro-fuzzy inference system with fuzzy c-means clustering (FCM-ANFIS) for Android malware classification. The proposed approach utilizes the FCM clustering method to determine the optimum number …
Turkish Entity Discovery With Word Embeddings, Murat Kalender, Emi̇n Erkan Korkmaz
Turkish Entity Discovery With Word Embeddings, Murat Kalender, Emi̇n Erkan Korkmaz
Turkish Journal of Electrical Engineering and Computer Sciences
Entity-linking systems link noun phrase mentions in a text to their corresponding knowledge base entities in order to enrich a text with metadata. Wikipedia is a popular and comprehensive knowledge base that is widely used in entity-linking systems. However, long-tail entities are not popular enough to have their own Wikipedia articles. Therefore, a knowledge base created by using Wikipedia entities would be limited to only popular entities. In order to overcome the knowledge base coverage limitation of Wikipedia-based entity-linking systems, this paper presents an entity-discovery system that can detect semantic types of entities that are not defined in Wikipedia. The …
Evaluation Of Distance Education Websites: A Hybrid Multicriteria Approach, Mehmet Kabak, Eren Özceylan, Meti̇n Dağdevi̇ren, Tolga Genç
Evaluation Of Distance Education Websites: A Hybrid Multicriteria Approach, Mehmet Kabak, Eren Özceylan, Meti̇n Dağdevi̇ren, Tolga Genç
Turkish Journal of Electrical Engineering and Computer Sciences
This study provides an evaluation model that prioritizes the relative weights of various distance education websites. The proposed approach consists of four sequential steps. In the first step, 25 different subcriteria under 4 major criteria are gathered from the existing literature. Identified criteria are weighted by stakeholders of distance education websites using the analytic network process outlined in the second step. In the third step, the technique for order preference by similarity to ideal solution method is applied in order to rank the 15 different Turkish distance education website alternatives. Finally, in the fourth step, different scenario analyses are applied …