Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11413/6817
Browse
Browsing Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering by Publisher "Elsevier"
Now showing 1 - 5 of 5
- Results Per Page
- Sort Options
Publication Adaptive Direction-Guided Structure Tensor Total Variation(Elsevier, 2021) Kamasak, Mustafa E.; TÜREYEN, EZGİ DEMİRCANDirection-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the arbitrary (multi-directional and/or partly nondirectional) images. In this study, we build a two-stage denoising framework that brings adaptivity to the DSTV based denoising. We design a DSTV-like alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of quality and computational efficiency.Publication NeuroBioSense: A Multidimensional Dataset for Neuromarketing Analysis(Elsevier, 2024) KOCAÇINAR, BÜŞRA; İNAN, PELİN; ZAMUR, ELA NUR; ÇALŞİMŞEK, BUKET; AKBULUT, FATMA PATLAR; Çatal, ÇağatayIn the context of neuromarketing, sales, and branding, the investigation of consumer decision-making processes presents complex and intriguing challenges. Consideration of the effects of multicultural influences and societal conditions from a global perspective enriches this multifaceted field. The application of neuroscience tools and techniques to international marketing and consumer behavior is an emerging interdisciplinary field that seeks to understand the cognitive processes, reactions, and selection mechanisms of consumers within the context of branding and sales. The NeuroBioSense dataset was prepared to analyze and classify consumer responses. This dataset includes physiological signals, facial images of the participants while watching the advertisements, and demographic information. The primary objective of the data collection process is to record and analyze the responses of human subjects to these signals during a carefully designed experiment consisting of three distinct phases, each of which features a different form of branding advertisement. Physiological signals were collected with the Empatica e4 wearable sensor device, considering non-invasive body photoplethysmography (PPG), electrodermal activity (EDA), and body temperature sensors. A total of 58 participants, aged between 18 and 70, were divided into three different groups, and data were collected. Advertisements prepared in the categories of cosmetics for 18 participants, food for 20 participants, and cars for 20 participants were watched. On the emotion evaluation scale, 7 different emotion classes are given: Joy, Surprise, anger, disgust, sadness, fear, and neutral. This dataset will help researchers analyse responses, understand and develop emotion classification studies, the relationship between consumers and advertising, and neuromarketing methods.Publication Neurophysiological and Biosignal Data for Investigating Occupational Mental Fatigue: MEFAR Dataset(Elsevier, 2024) Derdiyok, Şeyma; AKBULUT, FATMA PATLAR; Çatal, ÇağatayThe prevalence of mental fatigue is a noteworthy phenomenon that can affect individuals across diverse professions and working routines. This paper provides a comprehensive dataset of physiological signals obtained from 23 participants during their professional work and questionnaires to analyze mental fatigue. The questionnaires included demographic information and Chalder Fatigue Scale scores indicating mental and physical fatigue. Both physiological signal measurements and the Chalder Fatigue Scale were performed in two sessions, morning and evening. The present dataset encompasses diverse physiological signals, including electroencephalogram (EEG), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and 3-axis accelerometer (ACC) data. The NeuroSky MindWave EEG device was used for brain signals, and the Empatica E4 smart wristband was used for other signals. Measurements were carried out on individuals from four different occupational groups, such as academicians, technicians, computer engineers, and kitchen workers. The provision of comprehensive metadata supplements the dataset, thereby promoting inquiries about the neurophysiological concomitants of mental fatigue, autonomic activity patterns, and the repercussions of a cognitive burden on human proficiency in actual workplace settings. The accessibility of the aforementioned dataset serves to facilitate progress in the field of mental fatigue research while also laying the groundwork for the creation of customized fatigue evaluation techniques and interventions in diverse professional domains.Publication Wearable Sensor-Based Evaluation of Psychosocial Stress in Patients With Metabolic Syndrome(Elsevier, 2020) AKBULUT, FATMA PATLAR; İkitimur, Barış; Akan, AydınThe prevalence of metabolic disorders has increased rapidly as such they become a major health issue recently. Despite the definition of genetic associations with obesity and cardiovascular diseases, they constitute only a small part of the incidence of disease. Environmental and physiological effects such as stress, behavioral and metabolic disturbances, infections, and nutritional deficiencies have now revealed as contributing factors to develop metabolic diseases. This study presents a multivariate methodology for the modeling of stress on metabolic syndrome (MES) patients. We have developed a supporting system to cope with MES patients' anxiety and stress by means of several biosignals such as ECG, GSR, body temperature, SpO(2), glucose level, and blood pressure that are measured by a wearable device. We employed a neural network model to classify emotions with HRV analysis in the detection of stressor moments. We have accurately recognized the stressful situations using physiological responses to stimuli by utilizing our proposed affective state detection algorithm. We evaluated our system with a dataset of 312 biosignal records from 30 participants and the results showed that our proposed method achieved an average accuracy of 92% and 89% in distinguishing stress level in MES and other groups respectively. Both being the focus of an MES group and others proved to be highly arousing experiences which were significantly reflected in the physiological signal. Exposure to the stress in MES and cardiovascular heart disease patients increases the chronic symptoms. An early stage of comprehensive intervention may reduce the risk of general cardiovascular events in these particular groups. In this context, the use of e-health applications such as our proposed system facilitates these processes.Publication Web Service Discovery: Rationale, Challenges, and Solution Directions(Elsevier, 2023) TOKMAK, AHMET VEDAT; AKBULUT, AKHAN; Çatal, ÇağatayService Oriented Architecture (SOA) is a methodology that promotes cooperation between services with diverse, but connected functions. Web Service technology paved the way for microservice architecture as it is a feature of modern web applications that resulted from the rise of SOA. With the proliferation of self-contained services, the ease of finding has emerged as a critical concern. Due to the increasing number of services that perform identical tasks, it has become difficult for users to select the most feasible service. Providing the most relevant service for the customer quickly is a crucial infrastructure task, and undiscovered services increase ecosystem expenses. Syntactic, semantic-conscious, and ontology-based studies have been presented as ways to improve the effectiveness and quality of service discovery techniques. While there are many approaches that have been proposed and validated for service discovery in literature, these studies are fragmented and there is a lack of overview of the techniques of web service discovery. As such, we conduct a Systematic Literature Review (SLR) study to review the existing body of knowledge surrounding service discovery and discuss the state-of-the-art. We present an overview of the techniques and empirical evidence by identifying, analyzing, and classifying the papers. Among the 764 papers we retrieved, 54 papers were included. We provide a comprehensive analysis of methodologies and tools for discovering web services.