Computer Engineering Commons^™

Data Science Applied To Discover Ancient Minoan-Indus Valley Trade Routes Implied By Commonweight Measures, Peter Revesz

CSE Conference and Workshop Papers

This paper applies data mining of weight measures to discover possible long-distance trade routes among Bronze Age civilizations from the Mediterranean area to India. As a result, a new northern route via the Black Sea is discovered between the Minoan and the Indus Valley civilizations. This discovery enhances the growing set of evidence for a strong and vibrant connection among Bronze Age civilizations.

A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …

Multitask-Based Association Rule Mining, Peli̇n Yildirim Taşer, Kökten Ulaş Bi̇rant, Derya Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and …

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering …

Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo

Electronic Thesis and Dissertation Repository

The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

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 …

Heart Attack Mortality Prediction: An Application Of Machine Learning Methods, Issam Salman

Turkish Journal of Electrical Engineering and Computer Sciences

The heart is an important organ in the human body, and acute myocardial infarction (AMI) is the leading cause of death in most countries. Researchers are doing a lot of data analysis work to assist doctors in predicting the heart problem. An analysis of the data related to different health problems and its functions can help in predicting the wellness of this organ with a degree of certainty. Our research reported in this paper consists of two main parts. In the first part of the paper, we compare different predictive models of hospital mortality for patients with AMI. All results …

Domain-Specific Use Cases For Knowledge-Enabled Social Media Analysis, Soon Jye Kho, Swati Padhee, Goonmeet Bajaj, Krishnaprasad Thirunarayan, Amit Sheth

Publications

No abstract provided.

Real-Time Power System Dynamic Security Assessment Based On Advanced Feature Selection For Decision Tree Classifiers, Qusay Al-Gubri, Mohd Aifaa Mohd Ariff

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a novel algorithm based on an advanced feature selection technique for the decision tree (DT) classifier to assess the dynamic security in a power system. The proposed methodology utilizes symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier-based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of the DT classifier. The effectiveness of the proposed technique is demonstrated on the modified IEEE 30-bus test system model. The results show that the DT classifier with …

Dtreesim: A New Approach To Compute Decision Tree Similarity Using Re-Mining, Gözde Bakirli, Derya Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

A number of recent studies have used a decision tree approach as a data mining technique; some of them needed to evaluate the similarity of decision trees to compare the knowledge reflected in different trees or datasets. There have been multiple perspectives and multiple calculation techniques to measure the similarity of two decision trees, such as using a simple formula or an entropy measure. The main objective of this study is to compute the similarity of decision trees using data mining techniques. This study proposes DTreeSim, a new approach that applies multiple data mining techniques (classification, sequential pattern mining, and …

Discovering The Relationships Between Yarn And Fabric Properties Using Association Rule Mining, Peli̇n Yildirim, Derya Bi̇rant, Tuba Alpyildiz

Turkish Journal of Electrical Engineering and Computer Sciences

Investigation of the effects of yarn parameters on fabric quality and finding important parameters to achieve desired fabric properties are important issues for the design process with the aim to meet the needs of the textile industry and the consumer for complex and specific requirements of functionality. Despite many statistical and mathematical studies that predict and reveal specific properties of utilized yarn and fabric materials, a number of challenges continue to exist when evaluated in many perspectives, such as discovering complex relationships among material properties in data. Data mining plays an important role in discovering hidden patterns from fabric data …

An Ant Colony Optimization Algorithm-Based Classification For The Diagnosis Of Primary Headaches Using A Website Questionnaire Expert System, Ufuk Çeli̇k, Ni̇lüfer Yurtay

Turkish Journal of Electrical Engineering and Computer Sciences

The purpose of this research was to evaluate the classification accuracy of the ant colony optimization algorithm for the diagnosis of primary headaches using a website questionnaire expert system that was completed by patients. This cross-sectional study was conducted in 850 headache patients who randomly applied to hospital from three cities in Turkey with the assistance of a neurologist in each city. The patients filled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classification results of an ant colony optimization-based classification algorithm. The ant colony algorithm for diagnosis classified patients with 96.9412% overall accuracy. …

Proposing A New Clustering Method To Detect Phishing Websites, Morteza Arab, Mohammad Karim Sohrabi

Turkish Journal of Electrical Engineering and Computer Sciences

Phishing websites are fake ones that are developed by ill-intentioned people to imitate real and legal websites. Most of these types of web pages have high visual similarities to hustle the victims. The victims of phishing websites may give their bank accounts, passwords, credit card numbers, and other important information to the designers and owners of phishing websites. The increasing number of phishing websites has become a great challenge in e-business in general and in electronic banking specifically. In the present study, a novel framework based on model-based clustering is introduced to fight against phishing websites. First, a model is …

Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker

Turkish Journal of Electrical Engineering and Computer Sciences

The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation …

Automatic Classification Of Harmonic Data Using $K$-Means And Least Square Support Vector Machine, Hüseyi̇n Eri̇şti̇, Vedat Tümen, Özal Yildirim, Belkis Eri̇şti̇, Yakup Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using $k$-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a $k$-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the $k$-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid …

Using The K-Means Clustering Algorithm To Classify Features For Choropleth Maps, Mark Polczynski, Michael Polczynski

Electrical and Computer Engineering Faculty Research and Publications

Common methods for classifying choropleth map features typically form classes based on a single feature attribute. This technical note reviews the use of the k-means clustering algorithm to perform feature classification using multiple feature attributes. The k-means clustering algorithm is described and compared to other common classification methods, and two examples of choropleth maps prepared using k-means clustering are provided.

Hot Zone Identification: Analyzing Effects Of Data Sampling On Spam Clustering, Rasib Khan, Mainul Mizan, Ragib Hasan, Alan Sprague

Journal of Digital Forensics, Security and Law

Email is the most common and comparatively the most efficient means of exchanging information in today's world. However, given the widespread use of emails in all sectors, they have been the target of spammers since the beginning. Filtering spam emails has now led to critical actions such as forensic activities based on mining spam email. The data mine for spam emails at the University of Alabama at Birmingham is considered to be one of the most prominent resources for mining and identifying spam sources. It is a widely researched repository used by researchers from different global organizations. The usual process …

M-Fdbscan: A Multicore Density-Based Uncertain Data Clustering Algorithm, Atakan Erdem, Taflan İmre Gündem

Turkish Journal of Electrical Engineering and Computer Sciences

In many data mining applications, we use a clustering algorithm on a large amount of uncertain data. In this paper, we adapt an uncertain data clustering algorithm called fast density-based spatial clustering of applications with noise (FDBSCAN) to multicore systems in order to have fast processing. The new algorithm, which we call multicore FDBSCAN (M-FDBSCAN), splits the data domain into c rectangular regions, where c is the number of cores in the system. The FDBSCAN algorithm is then applied to each rectangular region simultaneously. After the clustering operation is completed, semiclusters that occur during splitting are detected and merged to …

Discovery Of Hydrometeorological Patterns, Mete Çeli̇k, Fi̇li̇z Dadaşer Çeli̇k, Ahmet Şaki̇r Dokuz

Turkish Journal of Electrical Engineering and Computer Sciences

Hydrometeorological patterns can be defined as meaningful and nontrivial associations between hydrological and meteorological parameters over a region. Discovering hydrometeorological patterns is important for many applications, including forecasting hydrometeorological hazards (floods and droughts), predicting the hydrological responses of ungauged basins, and filling in missing hydrological or meteorological records. However, discovering these patterns is challenging due to the special characteristics of hydrological and meteorological data, and is computationally complex due to the archival history of the datasets. Moreover, defining monotonic interest measures to quantify these patterns is difficult. In this study, we propose a new monotonic interest measure, called the hydrometeorological …

An Urgent Precaution System To Detect Students At Risk Of Substance Abuse Through Classification Algorithms, Faruk Bulut, İhsan Ömür Bucak

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide. The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students. After becoming addicted to drugs, life becomes unbearable and gets even worse for their users. Scientific studies show that it becomes extremely difficult for an individual to break this habit after being a user. Hence, preventing teenagers from addiction becomes an important issue. This study focuses on an urgent precaution system that helps families and educators prevent teenagers from developing …

A Knowledge-Based Clinical Toxicology Consultant For Diagnosing Multiple Exposures, Joel D. Schipper, Douglas D. Dankel Ii, A. Antonio Arroyo, Jay L. Schauben

Publications

Objective: This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multiple toxins, multiple exposures account for more than half of all toxin-related fatalities. Using simple medical mathematics, we seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances.

Methods: The system is automatically trained using data mining …

A Rule Induction Algorithm For Knowledge Discovery And Classification, Ömer Akgöbek

Turkish Journal of Electrical Engineering and Computer Sciences

Classification and rule induction are key topics in the fields of decision making and knowledge discovery. The objective of this study is to present a new algorithm developed for automatic knowledge acquisition in data mining. The proposed algorithm has been named RES-2 (Rule Extraction System). It aims at eliminating the pitfalls and disadvantages of the techniques and algorithms currently in use. The proposed algorithm makes use of the direct rule extraction approach, rather than the decision tree. For this purpose, it uses a set of examples to induce general rules. In this study, 15 datasets consisting of multiclass values with …

Knowledge Discovery And Analysis In Manufacturing, Mark Polczynski, Andzrej Kochanski

Electrical and Computer Engineering Faculty Research and Publications

The quality and reliability requirements for next-generation manufacturing are reviewed, and current approaches are cited. The potential for augmenting current quality/reliability technology is described, and characteristics of potential future directions are postulated. Methods based on knowledge discovery and analysis in manufacturing (KDAM) are reviewed, and related successful applications in business and social fields are discussed. Typical KDAM applications are noted, along with general functions and specific KDAM-related technologies. A systematic knowledge discovery process model is reviewed, and examples of current work are given, including description of successful applications of KDAM to creation of rules for optimizing gas porosity in sand …

Diagnostics Of Eccentricities And Bar/End-Ring Connector Breakages In Polyphase Induction Motors Through A Combination Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Techniques, John F. Bangura, Richard J. Povinelli, Nabeel Demerdash, Ronald H. Brown

Electrical and Computer Engineering Faculty Research and Publications

This paper develops the foundations of a technique for detection and categorization of dynamic/static eccentricities and bar/end-ring connector breakages in squirrel-cage induction motors that is not based on the traditional Fourier transform frequency-domain spectral analysis concepts. Hence, this approach can distinguish between the "fault signatures" of each of the following faults: eccentricities, broken bars, and broken end-ring connectors in such induction motors. Furthermore, the techniques presented here can extensively and economically predict and characterize faults from the induction machine adjustable-speed drive design data without the need to have had actual fault data from field experience. This is done through the …

A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events, Richard J. Povinelli, Xin Feng

Electrical and Computer Engineering Faculty Research and Publications

A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundamental concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The …

Diagnostics Of Bar And End-Ring Connector Breakage Faults In Polyphase Induction Motors Through A Novel Dual Track Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Modeling, Richard J. Povinelli, John F. Bangura, Nabeel Demerdash, Ronald H. Brown

Electrical and Computer Engineering Faculty Research and Publications

This paper develops the fundamental foundations of a technique for detection of faults in induction motors that is not based on the traditional Fourier transform frequency domain approach. The technique can extensively and economically characterize and predict faults from the induction machine adjustable speed drive design data. This is done through the development of dual-track proof-of-principle studies of fault simulation and identification. These studies are performed using our proven Time Stepping Coupled Finite Element-State Space method to generate fault case data. Then, the fault cases are classified by their inherent characteristics, so-called “signatures” or “fingerprints.” These fault signatures are extracted …