Digital Commons Network^™

Simulating And Modelling Opinion Dynamics, Jennifer Heermance

Graduate Research Theses & Dissertations

The foundation of social media is conversation. Social media allows people to share ideas and opinions, as well as discuss those opinions. A point of intrigue for many social scientists is how those opinions change through interaction with others. What influences someone’s opinion? When is a person willing to adapt their opinion, and when does it remain the same? Is it possible to measure these opinion dynamics? Our overall goal is to develop a more comprehensive model for opinion dynamics. The first step of this process is to simulate data that can then be analyzed and used to develop a …

Using Machine Learning Models To Discover Promising Research, Akhil Pandey Akella

Graduate Research Theses & Dissertations

Endeavors to identify valuable research involve the factors of discovery, comprehensibility, and reproducibility. The purpose of this study is to assist scholars in finding research that is both promising and of high quality. I explain how we can approach the problem of reproducibility in relation to scholarly articles and propose gauging the public understanding of science as a way to determine the comprehensibility of given research articles. Additionally, I explain how the concept of long-term social media impact supports the discovery of scholarly articles likely to be impactful even with the passage of time. I build and describe machine-learning models …

Morphometric Analysis Of Subfossil Macronycteris Spp. (Chiroptera: Hipposideridae) From Madagascar, Jamie Lynn Alumbaugh

Graduate Research Theses & Dissertations

Macronycteris bats are morphologically conservative between species but demonstrate intraspecific morphological variation between geographic locations and sexes. Two of the four living species of Macronycteris are found on Madagascar, where they are broadly distributed and demonstrate a trend in body size correlated with the latitudinal precipitation cline on the western side of the island. The presence of an extinct species, M. besaoaka, from Anhjohibe Cave in northern Madagascar suggests that Macronycteris was once more diverse, at least with respect to morphology. Since its description, taxonomic and phylogenetic revisions have reshaped our understanding of this genus. On Madagascar, these include the …

Using Reinforcement Learning In A Simulated Intelligent Tutoring System, Manohar Sai Jasti

Graduate Research Theses & Dissertations

I used reinforcement learning to investigate which categories of hints are most efficient in an intelligent tutoring system for human anatomy. Efficiency is defined as minimizing the time it takes the student to learn the material. When a student gives a wrong answer, the tutor can give them a text hint, a diagrammatic hint, or a video clip. Each type of hint takes a different amount of time to deliver and takes the student a different amount of time to understand.

I built a simulator for the intelligent tutoring system to collect data from simulated students. I implemented reinforcement learning, …

Leveraging Nlp And Social Network Analytic Techniques To Detect Censored Keywords: System Design And Experiments, Christopher S. Leberknight, Anna Feldman

Department of Computer Science Faculty Scholarship and Creative Works

Internet regulation in the form of online censorship and Internet shutdowns have been increasing over recent years. This paper presents a natural language processing (NLP) application for performing cross country probing that conceals the exact location of the originating request. A detailed discussion of the application aims to stimulate further investigation into new methods for measuring and quantifying Internet censorship practices around the world. In addition, results from two experiments involving search engine queries of banned keywords demonstrates censorship practices vary across different search engines. These results suggest opportunities for developing circumvention technologies that enable open and free access to …

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 …

The Economic And Social Values Associated With Small South Dakota Lakes, Aaron Patrick Sundmark

Electronic Theses and Dissertations

The valuation of small fishing lakes is a vital component in understanding the importance of fishing and of recreational resources, in general. Knowing the values associated with such lakes is essential when prioritizing management activities. The overall value of a lake as a resource for human benefit is estimated as the summation of both instrumental and non-instrumental values. Instrumental values consist of economic and utilitarian values, as well as the values that a lake provides from ecosystem services. Non-instrumental values consider what the lake is worth as a good of its own, such as aesthetic, moral, and spiritual values gained …

Analysing The Impact Of Machine Learning To Model Subjective Mental Workload: A Case Study In Third-Level Education, Karim Moustafa, Luca Longo

Conference papers

Mental workload measurement is a complex multidisciplinary research area that includes both the theoretical and practical development of models. These models are aimed at aggregating those factors, believed to shape mental workload, and their interaction, for the purpose of human performance prediction. In the literature, models are mainly theory-driven: their distinct development has been influenced by the beliefs and intuitions of individual scholars in the disciplines of Psychology and Human Factors. This work presents a novel research that aims at reversing this tendency. Specifically, it employs a selection of learning techniques, borrowed from machine learning, to induce models of mental …

Fuzzy-Logic-Based Robust Speed Control Of Switched Reluctance Motor For Low And High Speeds, Zeki̇ Omaç

Turkish Journal of Electrical Engineering and Computer Sciences

Switched reluctance motor (SRM) is operated at high magnetic saturation to generate large torque. The flux linkage of SRM is a nonlinear function with phase current and rotor position because of the high magnetic saturation. Also, the performance of the speed controller for the SRM driver system can be negatively affected by noise, disturbances, and inertia of load torque. Therefore, the fuzzy speed controller for the SRM driver system was developed in this study. In addition, a dynamic model of SRM was simulated in Matlab/Simulink software. Based on the results obtained in this study, the speed of the SRM was …

Methods For Classification Of Consumer Review Into The Ones Written Before Or After The Product Purchase, Md Mehedi Hasan

Wayne State University Theses

Online consumer reviews provide a wealth of information about products and services that, if properly identified and extracted, could be of immense value to businesses. While classification of reviews according to sentiment polarity has been extensively studied in previous work, many more focused types of review analysis remain open problems. In this work, we introduce a novel text classification problem of separating post-purchase from pre-purchase consumer review fragments that can facilitate identification of immediate actionable insights based on the feedback from the customers, who actually purchased and own a product. To address this problem, we propose the features, which are …

The Impact Of Cross-References On The Readability Of The U.S. Internal Revenue Code, Jeffrey A. Lasky

CCAC Theses and Dissertations

Scholars and practitioners have long argued that U.S. income tax law (“the Tax Code”) is excessively complex and difficult to understand, and hence imposes non-trivial adjudication, administration, planning, and compliance costs across the spectrum of income tax stakeholders: the courts, the Internal Revenue Service, tax practitioners, business managers, and individual taxpayers. Hence, there is considerable interest in reducing the effort needed to accurately understand and apply the provisions of income tax law. Prior scholarly work has strongly argued that exceptions to Tax Code provisions as expressed by cross-references embedded in the Tax Code text constitute a major source of reading …

Efficient Algorithms For Mining Healthcare Data :, Yan Hu

Legacy Theses & Dissertations (2009 - 2024)

Data-Driven Healthcare (DDH) is defined as the usage of available medical big data to provide the best and most personalized care, which is believed to be one of the most promising directions for transforming healthcare. The healthcare data includes claims and cost data, clinical data, pharmaceutical R&D data, patient behavior and sentiment data, and health data on the web. There has been a remarkable upsurge in the adoption of healthcare data over the past several years. In particular, it has been used for medical concept extraction, patient trajectory modeling, disease inference, etc.

Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar

Legacy Theses & Dissertations (2009 - 2024)

Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …

Prioritizing Interdependent Software Requirements Using Tensor And Fuzzy Graphs, Negin Misaghian, Homayun Motameni, Mohsen Rabbani

Turkish Journal of Electrical Engineering and Computer Sciences

Owing to the special stance of prioritizing tasks in requirements engineering processes, and as the requirements are not independent in nature, considering their dependencies is essential during the prioritizing process. Although different classifications of dependency types among requirements exist, only a few approaches in the prioritization process consider such valuable data (dependency among equirements). To achieve a practical prioritization, this study proposes a method based on the effects of the requirement dependencies (increase/decrease cost of) on the value of prioritization provided by the tensor concept. Since the strengths of dependencies are also influential factors in the act of prioritization, The …

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, …

Segmented Character Recognition Using Curvature-Based Global Image Feature, Belaynesh Chekol, Numan Çelebi̇, Tuğrul Taşci

Turkish Journal of Electrical Engineering and Computer Sciences

Character recognition in natural scene images is a fundamental prerequisite for many text-based image analysis tasks. Generally, local image features are employed widely to recognize characters segmented from natural scene images. In this paper, a curvature-based global image feature and description for segmented character recognition is proposed. This feature is entirely dependent on the curvature information of the image pixels. The proposed feature is employed for segmented character recognition using Chars74k dataset and ICDAR 2003 character recognition dataset. From the two datasets, 1068 and 540 images of characters, respectively, are randomly chosen and 573-dimensional feature vector is synthesized per image. …

A Hybrid Of Fuzzy Theory And Quadratic Function For Estimating And Refining Transmission Map, Jyun-Yu Jhang, Chengjian Lin, Kuu-Young Young, Chin-Teng Lin

Turkish Journal of Electrical Engineering and Computer Sciences

In photographs captured in outdoor environments, particles in the air cause light attenuation and degrade image quality. This effect is especially obvious in hazy environments. In this study, a fuzzy theory is proposed to estimate the transmission map of a single image. To overcome the problem of oversaturation in dehazed images, a quadratic-function-based method is proposed to refine the transmission map. In addition, the color vector of the atmospheric light is estimated using the top 1\% of the brightest light area. Finally, the dehazed image is reconstructed using the transmission map and the estimated atmospheric light. Experimental results demonstrate that …

A Robust Smes Control For Enhancing Stability Of Distribution Systems Fed From Intermittent Wind Power Generation, Sayed Said, Mokhtar Aly, Balint Hartmann

Turkish Journal of Electrical Engineering and Computer Sciences

The voltage and frequency stability issues of power systems are the main challenges that arise from high penetration levels of wind energy systems. This paper presents an effective solution for voltage and frequency stability problems by using a superconducting magnetic energy storage (SMES) system controlled with a fuzzy logic controller (FLC). The proposed control system can suppress the voltage and frequency fluctuations due to the high variations of wind speed. In addition, the proposed control system is suitable for both balanced and unbalanced distribution systems with high penetration levels of wind turbines (WTs). A squirrel cage induction generator (SCIG) is …

A New Model To Determine The Hierarchical Structure Of The Wireless Sensor Networks, Resmi̇ye Nasi̇boğlu, Zülküf Teki̇n Erten

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless sensor networks are one of the rising areas of scientific research. Common purpose of these investigations is usually constructing optimal structure of the network by prolonging its lifetime. In this study, a new model has been proposed to construct a hierarchical structure of wireless sensor networks. Methods used in the model to determine clusters and appropriate cluster heads are k-means clustering and fuzzy inference system (FIS), respectively. The weighted averaging based on levels (WABL) defuzzification method is used to calculate crisp outputs of the FIS. A new theorem for calculation of WABL values has been proved in order to …

A Fuzzy Model Of Directional Relationships From The Phi-Descriptor, Mohammad Naeem, Nasir Ahmad, Sahib Khan, Asma Gul

Turkish Journal of Electrical Engineering and Computer Sciences

Directional spatial relationships are a category of spatial relationships and have applications in the fields of image processing, geographic information systems, natural language processing, and robot navigation. They can be directly extracted from images or can be interpreted from a type of image descriptors called the relative position descriptors. Examples of relative position descriptors are the angle histogram, the force histogram, and the recently proposed phi-descriptor. So far, fuzzy models of directional spatial relationships from the angle histogram and force histograms have been proposed in the literature. These include the compatibility method, the aggregation method, and the method of effective …

Data Analysis Through Social Media According To The Classified Crime, Serkan Savaş, Nuretti̇n Topaloğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The amount and variety of data generated through social media sites has increased along with the widespread use of social media sites. In addition, the data production rate has increased in the same way. The inclusion of personal information within these data makes it important to process the data and reach meaningful information within it. This process can be called intelligence and this meaningful information may be for commercial, academic, or security purposes. An example application is developed in this study for intelligence on Twitter. Crimes in Turkey are classified according to Turkish Statistical Institute criminal data and keywords are …

A Tree-Based Approach For English-To-Turkish Translation, Özge Bakay, Begüm Avar, Olcay Taner Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we present our English-to-Turkish translation methodology, which adopts a tree-based approach. Our approach relies on tree analysis and the application of structural modification rules to get the target side (Turkish) trees from source side (English) ones. We also use morphological analysis to get candidate root words and apply tree-based rules to obtain the agglutinated target words. Compared to earlier work on English-to-Turkish translation using phrase-based models, we have been able to obtain higher BLEU scores in our current study. Our syntactic subtree permutation strategy, combined with a word replacement algorithm, provides a 67 % relative improvement from …

Automatic Concept Identification Of Software Requirements In Turkish, Fatma Bozyi̇ği̇t, Özlem Aktaş, Deni̇z Kilinç

Turkish Journal of Electrical Engineering and Computer Sciences

Software requirements include description of the features for the target system and express the expectations of users. In the analysis phase, requirements are transformed into easy-to-understand conceptual models that facilitate communication between stakeholders. Although creating conceptual models using requirements is mostly implemented manually by analysts, the number of models that automate this process has increased recently. Most of the models and tools are developed to analyze requirements in English, and there is no study for agglutinative languages such as Turkish or Finnish. In this study, we propose an automatic concept identification model that transforms Turkish requirements into Unified Modeling Language …

Turkish Lexicon Expansion By Using Finite State Automata, Mustafa Burak Öztürk, Burcu Can Buğlalilar

Turkish Journal of Electrical Engineering and Computer Sciences

Turkish is an agglutinative language with rich morphology. A Turkish verb can have thousands of different word forms. Therefore, sparsity becomes an issue in many Turkish natural language processing (NLP) applications. This article presents a model for Turkish lexicon expansion. We aimed to expand the lexicon by using a morphological segmentation system by reversing the segmentation task into a generation task. Our model uses finite-state automata (FSA) to incorporate orthographic features and morphotactic rules. We extracted orthographic features by capturing phonological operations that are applied to words whenever a suffix is added. Each FSA state corresponds to either a stem …

Lexicon-Based Emotion Analysis In Turkish, Mansur Alp Toçoğlu, Adi̇l Alpkoçak

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we proposed a lexicon for emotion analysis in Turkish for six emotional categories happiness, fear, anger, sadness, disgust, and surprise. Besides, we also investigated the effects of a lemmatizer and a stemmer, two term-weighting schemes, four lexicon enrichment methods, and a term selection approach for lexicon construction. To do this, we generated Turkish emotion lexicon based on a dataset, TREMO, containing 25,989 documents. We then preprocessed the documents to obtain dictionary and stem forms of each term using a lemmatizer and a stemmer. Afterwards, we proposed two different weighting schemes where term frequency, term-class frequency and mutual …

A Polarity Calculation Approach For Lexicon-Based Turkish Sentiment Analysis, Gökhan Yurtalan, Murat Koyuncu, Çi̇ğdem Turhan

Turkish Journal of Electrical Engineering and Computer Sciences

Sentiment analysis attempts to resolve the senses or emotions that a writer or speaker intends to send across to the people about an object or event. It generally uses natural language processing and/or artificial intelligence techniques for processing electronic documents and mining the opinion specified in the content. In recent years, researchers have conducted many successful sentiment analysis studies for the English language which consider many words and word groups that set emotion polarities arising from the English grammar structure, and then use datasets to test their performance. However, there are only a limited number of studies for the Turkish …

A Hybrid Sentiment Analysis Method For Turkish, Buket Erşahi̇n, Özlem Aktaş, Deni̇z Kilinç, Mustafa Erşahi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a hybrid methodology for Turkish sentiment analysis, which combines the lexicon-based and machine learning (ML)-based approaches. On the lexicon-based side, we use a sentiment dictionary that is extended with a synonyms lexicon. Besides this, we tackle the classification problem with three supervised classifiers, naive Bayes, support vector machines, and J48, on the ML side. Our hybrid methodology combines these two approaches by generating a new lexicon-based value according to our feature generation algorithm and feeds it as one of the features to machine learning classifiers. Despite the linguistic challenges caused by the morphological structure of Turkish, the …

Extending A Sentiment Lexicon With Synonym--Antonym Datasets: Swnettr++, Fati̇h Sağlam, Burkay Genç, Hayri̇ Sever

Turkish Journal of Electrical Engineering and Computer Sciences

In our previous studies on developing a general-purpose Turkish sentiment lexicon, we constructed SWNetTR-PLUS, a sentiment lexicon of 37K words. In this paper, we show how to use Turkish synonym and antonym word pairs to extend SWNetTR-PLUS by almost 33 % to obtain SWNetTR++, a Turkish sentiment lexicon of 49K words. The extension was done by transferring the problem into the graph domain, where nodes are words, and edges are synonym--antonym relations between words, and propagating the existing tone and polarity scores to the newly added words using an algorithm we have developed. We tested the existing and new lexicons …

Hydrogen Production System With Fuzzy Logic-Controlled Converter, Sali̇h Nacar, Seli̇m Öncü

Turkish Journal of Electrical Engineering and Computer Sciences

Electrolyte current must be controlled in the water electrolysis systems. For this purpose, the power converter for the cell stack of the electrolyzer used in industrial hydrogen production is realized. A series resonant converter, which is suitable for high input voltage and low output current applications, is used as power stage of the electrolyzer. The high-frequency transformer is used for the impedance matching. While the system is running, the electrical resistance of the electrolyzer changes continuously; thus, fuzzy logic controller (FLC) is used to control the output current of the power converter. In this study, a 700-W converter prototype is …

Fast Nonsingular Terminal Decoupled Sliding-Mode Control Utilizing Time-Varying Sliding Surfaces, Ferhun Yorgancioğlu, Soydan Redif

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a fast form of nonsingular, terminal, decoupled, sliding-mode control, which utilizes time-varying sliding surfaces, is proposed for a class of fourth-order, single-input, multioutput, nonlinear systems. The novel control law features a fast term, in the manner of fast terminal sliding-mode control, which markedly improves the finite-time sliding-mode convergence speed near zero. Numerical simulation results, which are illustrated with a cart-pole inverted pendulum system and a ball-beam system, demonstrate that the proposed control law achieves, in general, favorable transient response and lower steady-state errors compared to state-of-the-art decoupled terminal sliding-mode control methods.