Tugba Taskaya Temizel

Middle East Technical University | METU · Department of Data Informatics

Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantag...

Persuasion and its applications aim at positively changing human behavior and they work the best when they are tailored to individuals. Recent studies show that individuals could give different responses to the same persuasion strategies which lead to personalization of persuasion strategies for better effectiveness. This study investigates what pe...

In this study, we investigate the effects of social context, personal and mobile phone usage on the inference of work engagement/challenge levels of knowledge workers and their responsiveness to well-being related notifications. Our results show that mobile application usage is associated to the responsiveness and work engagement/challenge levels o...

This study investigates the impact of students’ motivation and personality traits on their academic performance in online and blended learning environments. It was conducted with students attending a mandatory introductory information technology course given in a university in Turkey. The Big Five Inventory and Motivated Strategies for Learning Que...

The purpose of this study is to develop an intervention framework based on video clickstream interactions for delivering superior user experience for video lectures. Apart from existing studies on data driven interventions, this study focuses on video clickstream interactions to identify timely interventions for creating interactive video lectures....

Identifying land use mix (LUM) in urban areas is challenging, often requiring extensive human intervention and fieldwork. Accurate classification of LUM is crucial for various disciplines, including urban planning, urban economics, and public health. This study addresses this need by employing Voronoi triangulation and an entropy-based LUM formula...

This study investigates whether the ingredients listed on restaurant menus can provide insights into a city's socioeconomic status. Using data from an online food delivery system, the study compares menu items with local education rates and rental prices. A machine learning model is developed to predict menu prices based on ingredients and socioeco...

Mobile applications have seamlessly integrated into our daily lives, and have witnessed increasing use in the healthcare industry. However , the absence of comprehensive regulations and preliminary assessments poses potential risks to users' health and safety. Existing literature largely relies on manual evaluation techniques that leverage Persuasi...

Mobile applications have seen a growing prevalence in the healthcare sector, yet the absence of comprehensive regulations and preliminary assessments can lead to significant frustration and time loss for users. To address this, Persuasive System Design (PSD) principles and the Mobile App Rating Scale (MARS) have emerged as popular tools for gauging...

This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We...

Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs), limiting their widespread acceptance beyond chat-bot applications. Despite ongoing efforts, hallucinations remain a prevalent challenge in LLMs. The detection of hallucinations itself is also a formidable task, frequently requiring manual l...

The purpose of this study was to investigate the use of predictive video analytics in online courses in the literature. A systematic literature review was performed based on a hybrid search strategy that included both database searching and backward snowballing. In total, 77 related publications published between 2011 and April 2023 were identified...

Social media has become popular for spreading and consuming information online. On the other hand, the high number of posts has increased the need for fact checking. In the COVID-19 pandemic, the lack of information on the disease paved the way for the spread of false information, negatively affecting public health and society. In this paper, a new...

Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students’ test per...

(Accepted Paper: https://bit.ly/ACCBARPaper) Although several binary classification performance metrics have been defined, a few of them are used for performance evaluation of classifiers and performance comparison/reporting in the literature. Specifically, F1 and Accuracy (ACC) are the most known and conventionally used metrics. Despite their popu...

Although few performance evaluation instruments have been used conventionally in different machine learning-based classification problem domains, there are numerous ones defined in the literature. This study reviews and describes performance instruments via formally defined novel concepts and clarifies the terminology. The study first highlights th...

Predicting transfer values of association football players, despite its importance, has been studied in a limited way in the literature. The existing approaches have mainly focused on explanatory models that cannot be used in predicting future values. In this paper, we propose a method where we fuse in-game performance data, player popularity metri...

(Accepted Paper: https://bit.ly/TasKar2021, Online Material: https://github.com/gurol/TasKar) This study covers almost the ultimate set of binary-classification performance instruments derived from four dimensions of a confusion matrix, namely true positives/negatives and false positives/negatives and enhances their representation by establishing a...

This paper proposes a systematic benchmarking method called BenchMetrics to analyze and compare the robustness of binary-classification performance metrics based on the confusion matrix for a crisp classifier. BenchMetrics, introducing new concepts such as meta-metrics (metrics about metrics) and metric-space, has been tested on fifteen well-known...

Purpose This paper proposes a framework that automatically assesses content coverage and information quality of health websites for end-users. Design/methodology/approach The study investigates the impact of textual and content-based features in predicting the quality of health-related texts. Content-based features were acquired using an evidence-...

Player performance evaluation is a challenging problem with multiple dimensions. Football (soccer) is the largest sports industry in terms of monetary value and it is paramount that teams can assess the performance of players for both financial and operational reasons. However, this is a difficult task, not only because performance differs from pos...

The aim of the study is to investigate whether individuals report the places they are attached to in location-based services, and whether there is a relationship between the attachment scores of these places and their corresponding check-in frequency information. A survey is conducted to measure the degree of place attachment of individuals based o...

BenchMetrics: inputs, stages, outputs, evaluation criteria/meta-metrics for metrics and metric-spaces. The method was tested for the benchmarking data for 13 metrics (Experiment-1) and 15 metrics with two recently proposed metrics (Experiment-2). The evaluated metrics are ranked according to overall robustness values. The experiments also provide s...

Understanding in which circumstances office workers take rest breaks is important for delivering effective mobile notifications and make inferences about their daily lifestyle, e.g., whether they are active and/or have a sedentary life. Previous studies designed for office workers show the effectiveness of rest breaks for preventing work-related co...

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as “opioid epidemic,” has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work...

The Data for Refugees (D4R) Challenge resulted in many insights related to the movement patterns of the Syrian refugees within Turkey. In this chapter, we summarize some of the important findings, and suggest policy recommendations for the main areas of the challenge. These recommendations are sometimes broad suggestions, as the policy intervention...

This study aims to shed light on various aspects of refugees’ lives in Turkey using mobile call data records of Türk Telekom, enriched with numerous local data sets. To achieve this, we made use of several statistical and data mining techniques in addition to a novel methodology to find home and work-time anchors of mobile phone users we developed....

Yaptığımız çalışmada mobil arama detay kayıtlarını kullanarak Suriyeli mültecilerin mobilite desenlerini farklı boyutlardan inceleyip, Suriyeli göçmen krizi ile ilgili içgörüler sunmaya, onların yaşamlarını iyileştirmek adına önerilerde bulunmaya çalıştık. Sağlanan bu büyük veri sayesinde, belki de nitel araştırma yöntemleri ile ulaşılması pek de...

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work...

This study aims to shed light on various aspects of refugees’ lives in Turkey using mobile call data records of Türk Telekom, which is enriched with numerous local data sets. To achieve this, we made use of several techniques in addition to a novel methodology we developed for this particular domain. Our results showed that refugees are highly mobi...

As the volume, variety, velocity aspects of big data are increasing, the other aspects such as veracity, value, variability, and venue could not be interpreted easily by data owners or researchers. The aspects are also unclear if the data is to be used in machine learning studies such as classification or clustering. This study proposes four techni...

Adversarial examples have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the test samples in order to fool the classifier. Adversarial attacks use these intentionally generated examples and they pose a security risk to the ma...

Many people view the food photos available in social media when choosing a restaurant. The attractiveness of these photos is an important factor in shaping initial impressions about a restaurant. There are some properties such as colour harmony, colour balance, and content of an image which constitutes the aesthetics of an image. Although there ar...

Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the testing samples in order to make classifier misclassify the given data. Adversarial attacks use these intentionally generated examples...

Temporal or spatio-temporal sequential pattern discovery is a well-recognized important problem in many domains such as seismology, criminology and finance. The majority of the current approaches are based on candidate generation which necessitates parameter tuning such as definition of a neighborhood, an interest measure and a threshold value to e...

(THIS IS AN OLD ARTICLE. Please cite/refer to our most recent and greatly improved publication for this paper at https://bit.ly/researchPToPI you may also be interested in our other publication https://bit.ly/researchBenchMetrics) Binary classification is one of the most frequent studies in applied machine learning problems in various domains, from...

With the rapid emergence of mobile technologies in recent years, mobile health (m-health) has become fundamental to healthcare. Persuasion strategies and behavior change support features are widely used in m-health applications to increase the effectiveness of these applications on users. However, in the literature, there is a lack of research to a...

In recent years, the presence or absence of behavior change techniques used in mobile applications for physical activity have been investigated by several authors in the context of content analysis and qualitative studies. However, users’ adoption and evaluation of application specific features remains to be an unexplored area. In this study, mobil...

The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. An experimental study was conducted through the use of a specifically designed mobile application, on users' mobile phones. The goals were; first, to learn about the users' personality and the applications they...

Persuasive messaging is used to change individuals' behaviours in a specific way, which are effective mechanisms in decision making and are mainly created based on persuasion strategies. Persuasive messaging has been applied in numerous contexts such as e-health, fund-raising. In this study, the effects of Cialdini's six persuasion principles (auth...

Many studies examining the relation between mobile phone use and the personality traits of individual users have concluded that a significant relationship exists with extraversion standing out as a common trait in this context. In addition, innovativeness plays a key role in the users’ adoption of technology and this has been studied within the dom...

The purpose of this study is to analyze how efficient online study groups can be formed among students based on their personality traits. A survey consisting of Ten Item Personality Inventory (TIPI) was conducted among the undergraduate students in a well-known university. Eighty-two students who did not know each other were assigned to 35 small on...

Office workers in today's world face repetitive movements and/or static postures, which cause work-related musculoskeletal disorders (WRMSDs). Health and Safety Agencies throughout Europe and USA have developed intervention strategies for preventing WRMSDs such as taking rest breaks regularly. Breaks in 60-90 minutes, and simple exercises for neck...

Coherent nature of crowd movement allows representing the crowd motion using sparse features. However, surveillance videos recorded at dierent periods of time are likely to have dierent crowd densities and motion characteristics. These varying scene properties necessitate use of dierent models for an eective representation of behaviour at dierent p...

Son yıllarda mobil teknolojiler günlük rutin işleri gerçekleştirmek üzere sıkça kullanılmaya başlanmıştır. Mobil teknolojilerin bu denli hızla yaygınlaşması ise birçok sektör açısından ve akademik çalışmalar için kullanıcı verisi elde etmek üzere güvenilir bir kaynak haline gelmiştir. Mobil telefonlardan elde edilen veriler ile kullanıcıların alışk...

Customer profiling in mobile commerce (m-commerce) domain has recently gained importance due to the increased proliferation of smartphones and tablets. One of the major challenges, confronting m-commerce developers is the need to know users’ perception of m-commerce applications in order to better design and deliver m-commerce services. In this pap...

The aim of the study is to investigate whether individuals re-port the places they are attached to in location-based services, and whether there is a relationship between the attachment scores of these places and their corresponding check-in fre-quency information. A survey is conducted to measure the degree of place attachment of individuals based...

Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent. In this paper, we propose a framework which assesses the timeliness of the content of health websites automatically by evidence base...

With the increasing focus on safety and security in public areas, anomaly detection in video surveillance systems has become increasingly more important. In this paper, we describe a method that models the temporal behavior and detects behavioral anomalies in the scene using probabilistic graphical models. The Coupled Hidden Markov Model (CHMM) met...

Surveillance of crowded public spaces and detection of anomalies from the video is important for public safety and security. While anomaly detection is possible by detection and tracking of individuals in low-density areas, such methods are not reliable in high-density crowded scenes. In this work we propose a holistic unsupervised approach to clus...

The incorporation of pharmacogenomics information into the drug dosing estimation formulations has been shown to increase the accuracy in drug dosing and decrease the frequency of adverse drug effects in many studies in the literature. In this paper, an estimation framework based on Bayesian Structural Equation Modeling which is driven by pharmacog...

Road traffic congestions are one of the major problems in highly populated cities. In the recent years, GPS based solutions have become popular since people are able to see the traffic flow on streets instantaneously. However such systems are lack of intelligent reasoning regarding the traffic problems. As a result, one cannot anticipate the traffi...

Road traffic congestions are one of the major problems in highly populated cities. In the recent years, GPS based solutions have become popular since people are able to see the traffic flow on streets instantaneously. However such systems are lack of intelligent reasoning regarding the traffic problems. As a result, one cannot anticipate the traffi...

In crowd surveillance systems, it is important to select the proper analysis algorithm considering the properties of the video content. The inappropriate algorithm selection may result in performance degradation and generation of false alarms. An important feature of crowd videos is the density of the crowd. While object detection and tracking base...

Surveillance cameras are playing more important role in our daily life with the increasing number of human population and surveillance cameras. While there are a myriad of methods for video analysis, they are generally designed for low-density areas. Running of these algorithms in crowded areas would not give expected results and results in high nu...

Objective: When searching for particular medical information on the internet the challenge lies in distinguishing the websites that are relevant to the topic, and contain accurate information. In this article, we propose a framework that automatically identifies and ranks diabetes websites according to their relevance and information quality based...

The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. Recommendation systems deal with ends such as movies and consumer goods that are consumed by users where similarity between consumer tastes is generally taken into account. On the other hand, recommendation sys...

Automated analysis of crowd behaviour using surveillance videos is an important issue for public security as it allows detection of potentially dangerous situations in crowds. Although there is a considerable amount of study in crowd behaviour analysis, the majority are limited in several ways. A few problems to mention are: limited real-time consi...

We studied relationship between spatio-temporal events where some of the events are triggering and some others are triggered.

Abstract— Extraction of crowd dynamics from video is the fundamental step for automatic detection of abnormal events. However, it is difficult to obtain sufficient performance with object tracking due to occlusions and insufficient resolution of the objects in the scene. As a result, optical flow or feature tracking methods are preferred in crowd v...

Anomaly detection from crowd videos is an issue that is becoming more important due to the difficulties in maintaining the public security in crowded places. Surveillance videos has a significant role for enabling the real time analysis of the captured events occurring in crowded places. This paper presents a method that detects anomalies in crowd...

In this article, parallel implementation of a real-time intelligent video surveillance system on Graphics Processing Unit (GPU) is described. The system is based on background subtraction and composed of motion detection, camera sabotage detection (moved camera, out-of-focus camera and covered camera detection), abandoned object detection, and obje...

This chapter addresses the technical challenges and experiences associated with the domain-related algorithms implemented on GPU architectures specifically by using CUDA and OpenCL with an emphasis on real-time issues and optimization. The importance of GPUs has recently been recognized for general-purpose applications such as video and image proce...

The importance of ballistic applications has been recently recognized due to the increasing crime and terrorism threats and incidents around the world. Ballistic image analysis is one of the application areas which requires immediate response with high precision from large databases. Here, the microscopic markings on cartridge case of a bullet obta...

Many manually populated very large databases suffer from data quality problems such as missing, inaccurate data and duplicate entries. A recently recognized data quality problem is that of disguised missing data which arises when an explicit code for missing data such as NA (Not Available) is not provided and a legitimate data value is used instead...

Many manually populated very large databases suffer from data quality problems such as missing, inaccurate data and duplicate entries. A recently recognized data quality problem is that of disguised missing data which arises when an explicit code for missing data such as NA (Not Available) is not provided and a legitimate data value is used instead...

The highway accident data provides valuable information such as accident black spot locations and their spatio-temporal change to the experts. With the help of this information, experts may take timely precautions in order to prevent the incidents from happening in the future. There is a challenging detail here. Highway accident data is often manua...

Teaching computer and information literacy subjects to students with diverse backgrounds is an instructional challenge. This study aims to identify the students' attitudes towards a first year computer and information literacy course. Several factors such as student gender and student's department have been taken into consideration in order to unde...

Today, students acquire basic computer skills at the early stages of their lives through several courses taken at schools before university and social online environments where they can engage with their friends. As a result, students may become more reluctant to attend similar courses in universities. A similar situation has arisen recently with a...

Local grammar approach relies on constructing polylexical units having frozen characteristics. It has recently been shown to be superior to other named entity extraction approaches including the probabilistic, the symbolic, and the hybrid approach in terms of being able to work with untagged corpora and has successfully been applied to English, Por...

Clustering techniques are widely used to give insight about the similarities/dissimilarities between data set items. Most algorithms require the user to tune parameters such as number of clusters or threshold for cut-off point in a dendrogram. Such parameters also affect the clustering quality. In a good quality cluster, the intra-cluster similarit...

Corpora of texts are used typically to study the structure and function of language. The distribution of various linguistic units, comprising texts in a corpus are used to make and test hypotheses relevant to different linguistic levels of description. News reports and editorials have been used extensively to populate corpora for studying language,...