Aydın, Mehmet Nafiz
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Mehmet Nafiz, Aydin
MEHMET NAFIZ AYDIN
Aydın, MEHMET NAFIZ
Mehmet Nafiz AYDIN
AYDIN, MEHMET NAFIZ
Mehmet Nafiz Aydın
Aydın, M.
Aydin,M.N.
Aydin M.
Aydin,Mehmet Nafiz
Aydin, Mehmet Nafiz
A., Mehmet Nafiz
Aydın, M. N.
Aydın,M.N.
Aydın, Mehmet Nafiz
Nafiz Aydin M.
M. Aydın
M. N. Aydın
AYDIN, Mehmet Nafiz
Aydın M.
A.,Mehmet Nafiz
Aydin, Mehmet
Aydin, Mehmet N.
Aydın, M.N.
MEHMET NAFIZ AYDIN
Aydın, MEHMET NAFIZ
Mehmet Nafiz AYDIN
AYDIN, MEHMET NAFIZ
Mehmet Nafiz Aydın
Aydın, M.
Aydin,M.N.
Aydin M.
Aydin,Mehmet Nafiz
Aydin, Mehmet Nafiz
A., Mehmet Nafiz
Aydın, M. N.
Aydın,M.N.
Aydın, Mehmet Nafiz
Nafiz Aydin M.
M. Aydın
M. N. Aydın
AYDIN, Mehmet Nafiz
Aydın M.
A.,Mehmet Nafiz
Aydin, Mehmet
Aydin, Mehmet N.
Aydın, M.N.
Job Title
Doç. Dr.
Email Address
Main Affiliation
Management Information Systems
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
15
LIFE ON LAND
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
14
LIFE BELOW WATER
4
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
3
GOOD HEALTH AND WELL-BEING
7
Research Products
17
PARTNERSHIPS FOR THE GOALS
1
Research Products
4
QUALITY EDUCATION
2
Research Products
2
ZERO HUNGER
5
Research Products
10
REDUCED INEQUALITIES
2
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
13
CLIMATE ACTION
0
Research Products
1
NO POVERTY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
8
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
2
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
5
GENDER EQUALITY
0
Research Products
Documents
63
Citations
677
h-index
15
Documents
50
Citations
414
Scholarly Output
66
Articles
28
Views / Downloads
503/5232
Supervised MSc Theses
13
Supervised PhD Theses
5
WoS Citation Count
186
Scopus Citation Count
336
WoS h-index
8
Scopus h-index
10
Patents
0
Projects
0
WoS Citations per Publication
2.82
Scopus Citations per Publication
5.09
Open Access Source
33
Supervised Theses
18
| Journal | Count |
|---|---|
| Applied Sciences | 4 |
| Computers and Electronics in Agriculture | 3 |
| Journal of research in business (online) | 2 |
| Alphanumeric Journal | 1 |
| Applied Sciences (Switzerland) | 1 |
Current Page: 1 / 7
Scopus Quartile Distribution
Competency Cloud
66 results
Scholarly Output Search Results
Now showing 1 - 10 of 66
Conference Object Citation - Scopus: 7A Country-Specific Analysis on Internet Interconnection Ecosystems(IEEE, 2018) Cakmak, Gorkem; Aydın, Mehmet NafizWith the proliferating number of diverse participants and destinations to reach the Internet construct has become more intricate to assay. Today Internet Service Providers (ISPs) establish resilient networks from multiple providers and broaden the number of peering links-as financially as viable. However the complex structure of the global Internet ecosystem and entwined roles of Internet players simply prevent us from conducting generalized models for grasping interconnections which could be applied globally regardless of the local surroundings. In this paper the global inter-domain Internet topology is scrutinized by the help of interconnection characteristics within a country-specific stance. Our study on the Internet ecosystems helps us highlight the non-uniformity of interconnections by using both 'real world' metrics and network science metrics. One of the significant findings that the analysis yields is that presence of well-established Internet Exchange Points (IXPs) in an interconnection ecosystem-besides the benefit of bolstering the peering fabric-increases the competitive nature of Internet transit market and boosts the inclination to multihome for stub networks thus increases the resilience of national Internet constructs. © 2017 IEEE.Conference Object The Effects of Social Media Content on Consumer Behavior: The Case of Instagram(2022) Oruç, Zemzem Selin; Aydın, Mehmet NafizUnderstanding consumer behavior and decisions on e-commerce are vital. Well-defined consumer behavior and investigating what influences that behavior on an online shopping journey is a key for an online seller. However, having insights on what affects consumer behavior and understanding the relationship among content and user is a complex problem. There are various aspects of social media content in this process that mediates the decisions and behavior of customers. This paper investigates consumer behavior in connection with social media content from the media richness theory perspective. In particular, the changes in the content and its effects on consumer engagement and interaction were analyzed by considering the changes in engagement rates and the number of interactions. For empirical testing, a case study is conducted in a start-up e-commerce company, called Freja Silver. The variations of content have been analyzed and data-driven results have been evaluated.Conference Object A Machine Learning Approach To Steel Sheet Production Surface Quality(Institute of Electrical and Electronics Engineers Inc., 2024) Öztürk, A.; Aydin, M.N.This study aims to develop a machine learning approach for defect evaluation in steel sheet production. The primary objective is to improve the defect decision process by integrating human knowledge with technical data. The paper uses a case study with data from 2020 and reviews the literature on steel surface defects, decision support systems, classification algorithms, and text mining. The study focuses on the detection and repair of defects, aiming to eliminate defects in production and optimize decisions related to defect detection and repair. The methodology of the study involves comparing different classification techniques and enhancing these results with text processing applications. The study concludes that the existence of text data improves the performance of the classification algorithms. © 2024 IEEE.Conference Object Citation - Scopus: 1Metadata Action Network Model for Cloud Based Development Environment(Springer, 2020) Aydın, Mehmet Nafiz; Perdahçı, Ziya Nazım; Şafak, İlker; van Hillegersberg, JosCloud-based software development solutions (entitled as Platform-as-a-Service, Low-Code platforms) have been promoted as a game changing paradigm backed by model-driven architecture and supported by various cloud-based services. With the engagement of a sheer number of platform users (experienced, novel, or citizen developers) these platforms generate invaluable data and that can be considered as user metadata actions. As cloud-based development solutions provide novice users with a new development experience (performing data actions that altogether leads to a successful software app), users often times face with uncertainty about development performance; how good or complete is app development? Thus, the issue addressed in this research is how to measure user performance by using digital trace data generated on the cloud platform from a Network Science perspective. This research proposes a novel approach to leveraging digital trace data on Platform-as-a-Service (PaaS) from a Network Science perspective. The proposed approach considers the importance of digital trace data as metadata actions on PaaS and introduces a network model (so-called Metadata Action Network), which is claimed to be the result of reconstruction of events of developer’s actions. We show suitability of the proposed approach to better understanding of real-world digital trace data on PaaS solution and elaborate basic performance analytics on a PaaS solution with research and practical implications.Conference Object Examination of Centrality in a Health Social Network(IOS Press, 2014) Alasan, Semiha N.; Sayın, Kamran Emre; Aydın, Mehmet NafizGrowing importance of health information platforms are acknowledges in recent studies. Such platforms are subject to discussion about an extent to which social network characteristics are realized. The platform under examination indeed demonstrates social network peculiarities. In this work we explore the nature of centrality in one of the leading health information networks in Europe. Among other findings we identify two nodes (representing patient and physician) are as the most important people in the network in terms of structural analysis Egregiously these nodes are connected with the other types only and exhibit worth noticing connection patterns. These connections have been discussed along with a medication advice seeking behavior.Article Citation - WoS: 21Citation - Scopus: 31Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik(Jmır Publıcatıons, Inc, 130 Queens Quay E, 2020) Akdur, Görkem; Aydın, Mehmet Nafiz; Akdur, GizdemBackground: Dietetics mobile health apps provide lifestyle tracking and support on demand. Mobile health has become a new trend for health service providers through which they have been shifting their services from clinical consultations to online apps. These apps usually offer basic features at no cost and charge a premium for advanced features. Although diet apps are now more common and have a larger user base, in general, there is a gap in literature addressing why users intend to use diet apps. We used Diyetkolik, Turkey's most widely used online dietetics platform for 7 years, as a case study to understand the behavioral intentions of users. Objective: The aim of this study was to investigate the factors that influence the behavioral intentions of users to adopt and use mobile health apps. We used the Technology Acceptance Model and extended it by exploring other factors such as price-value, perceived risk, and trust factors in order to assess the technology acceptance of users. Methods: We conducted quantitative research on the Diyetkolik app users by using random sampling. Valid data samples gathered from 658 app users were analyzed statistically by applying structural equation modeling. Results: Statistical findings suggested that perceived usefulness (P<.001), perceived ease of use (P<.001), trust (P<.001), and price-value (P<.001) had significant relationships with behavioral intention to use. However, no relationship between perceived risk and behavioral intention was found (P=.99). Additionally, there was no statistical significance for age (P=.09), gender (P=.98), or previous app use experience (P=.14) on the intention to use the app. Conclusions: This research is an invaluable addition to Technology Acceptance Model literature. The results indicated that 2 external factors (trust and price-value) in addition to Technology Acceptance Model factors showed statistical relevance with behavioral intention to use and improved our understanding of user acceptance of a mobile health app. The third external factor (perceived risk) did not show any statistical relevance regarding behavioral intention to use. Most users of the Diyetkolik dietetics app were hesitant in purchasing dietitian services online. Users should be frequently reassured about the security of the platform and the authenticity of the platform's dietitians to ensure that users' interactions with the dietitians are based on trust for the platform and the brand.Article Citation - WoS: 16Citation - Scopus: 22Design and Implementation of a Smart Beehive and Its Monitoring System Using Microservices in the Context of Iot and Open Data(Elsevier Sci Ltd, 2022) Aydin, Sahin; Aydin, Mehmet NafizIt is essential to keep honey bees healthy for providing a sustainable ecological balance. One way of keeping honey bees healthy is to be able to monitor and control the general conditions in a beehive and also outside of a beehive. Monitoring systems offer an effective way of accessing, visualizing, sharing, and managing data that is gathered from performed agricultural and livestock activities for domain stakeholders. Such systems have recently been implemented based on wireless sensor networks (WSN) and IoT to monitor the activities of honey bees in beehives as well. Scholars have shown considerable interests in proposing IoT- and WSN-based beehive monitoring systems, but much of the research up to now lacks in proposing appropriate architecture for open data driven beehive monitoring systems. Developing a robust monitoring system based on a contemporary software architecture such as microservices can be of great help to be able to control the activities of honey bees and more importantly to be able to keep them healthy in beehives. This research sets out to design and implementation of a sustainable WSN-based beehive monitoring platform using a microservice architecture. We pointed out that by adopting microservices one can deal with long-standing problems with heterogeneity, interoperability, scalability, agility, reliability, maintainability issues, and in turn achieve sustainable WSN-based beehive monitoring systems.Conference Object Analysis and Implications of the Giant Component for an Online Interactive Platform(Int Business Information Management Assoc-IBIMA, 2016) Aydın, Mehmet Nafiz; Perdahci, N. ZiyaThis research is concerned with practical and research challenges related to understanding the nature of online interactive platforms. So-called network science is adopted to investigate the very nature of these systems as complex systems. In this regard we examine an online interactive health network and show that the interactive platform examined exhibits essential structural properties that characterize most real complex networks. We basically look into the largest connected component so-called a giant component (GC) to better understand how the representative network has established. In particular we apply dynamic network analysis to investigate how the GC has evolved over time. We identify a particular pattern towards emerging a GC. Implications of the patterns have been elaborated from a management perspective. We recommend that the basic stages of the emergence of the GC might be of interest to platform managers while evaluating performance of online platforms.Article Citation - WoS: 28Citation - Scopus: 47Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of Covid-19 Outbreak in Italy(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Öğrenci, Arif SelçukUnsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.Article Citation - Scopus: 38A hybrid deep learning framework for unsupervised anomaly detection in multivariate spatio-temporal data(MDPI AG, 2020) Karadayi,Y.; Aydin,M.N.; Ög˘renci,A.S.Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection. © 2020 by the authors.
