Mehmet Nafiz Aydın

Aydın, Mehmet Nafiz

Name Variants

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.

Job Title

Doç. Dr.

Email Address

mehmet.aydin@khas.edu.tr

Main Affiliation

Management Information Systems

Sustainable Development Goals Report Points

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Scholarly Output

53

Articles

23

Citation Count

127

Supervised Theses

15 Scholarly Output Search Results

Now showing 1 - 10 of 53

Citation - WoS: 0
Analysis and Implications of the Giant Component for an Online Interactive Platform
(Int Business Information Management Assoc-IBIMA, 2016) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Perdahci, N. Ziya; Management Information Systems
This 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.
Citation - WoS: 13
Citation - Scopus: 17
Design 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; Aydın, Mehmet Nafiz; Aydin, Mehmet Nafiz; Management Information Systems
It 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.
Citation - Scopus: 31
A hybrid deep learning framework for unsupervised anomaly detection in multivariate spatio-temporal data
(MDPI AG, 2020) Aydın, Mehmet Nafiz; Aydin,M.N.; Ög˘renci,A.S.; Management Information Systems
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.
Citation - Scopus: 6
A Country-Specific Analysis on Internet Interconnection Ecosystems
(IEEE, 2018) Cakmak, Gorkem; Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Management Information Systems
With 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.
Citation - WoS: 19
Citation - Scopus: 29
Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik
(Jmır Publıcatıons, Inc, 130 Queens Quay E, 2020) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Akdur, Gizdem; Management Information Systems
Background: 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.
Citation - WoS: 24
Citation - Scopus: 38
Unsupervised 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; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk; Management Information Systems
Unsupervised 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.
Ground Truth in Network Communities and Metadata-Aware Community Detection: a Case of School Friendship Network
(2021) Perdahçı, Ziya Nazım; Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Kafkas, Kenan; Management Information Systems
Real-world networks are everywhere and can represent biological, technological, and social interactions. They constitute complicated structures in terms of type of things and their relations. Understanding the network requires better examination of the network structure that can be achieved at various scales including macro, meso, and micro. This research is concerned with meso scale for a student best friendship network where sub-structures in which groups of entities (students) take different functions. In this study we address the following research questions: To what extent would NeoSBM as a stochastic process underlie best friendship interaction and in turn ground truth interactions (i.e. reported best friendship)? Do metadata such as gender or class contribute to this understanding? How can one support school managers from a meta-data aware community detection perspective? Our findings suggest that metadata aware community detection can be an effective method in supporting decision-making for class formation and group formation for in and out school activities.
Citation - WoS: 0
Citation - Scopus: 0
Management Frameworks and Management System Standards in the Context of Integration and Unification: a Review and Classification of Core Building Blocks for Consilience
(Mdpi, 2025) Aydın, Mehmet Nafiz; Aydin, Mehmet Nafiz; Management Information Systems
Management frameworks (MFs) and management system standards (MSSs) are essential tools for improving organisational management practises. They inherently include a range of fundamental building blocks that facilitate the creation of structured management systems. However, these building blocks have not yet been holistically identified or unified into a consilient taxonomy. Addressing this research gap, this study conducts a comprehensive review of 415 academic papers and theses, 47 ISO MSSs, and 79 MFs sourced from scholarly databases and official publications. Utilising a novel heuristic methodology, this study integrates a literature review, clustering, text mining analytics, and an expert review to develop a Consilient Building Block Taxonomy (CBBT). This taxonomy categorises the foundational components of MFs and MSSs, presenting them as a structured framework that unifies these elements into a cohesive system. By providing a systematic classification, the CBBT serves as a foundation for the development of a Unified Singular Management System (USMS). The proposed taxonomy enhances operational coherence, strategic alignment, and efficiency by consolidating the core aspects of diverse management systems. This study concludes with insights into how the CBBT can be leveraged to achieve integration and unification in management practises, offering significant potential for both research and practical applications.
Citation - Scopus: 4
Validity Issues of Digital Trace Date for Platform as a Service: a Network Science Perspective
(Springer Verlag, 2018) Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Kariniasukaite, Dzordana; Perdahci, Ziya N.; Management Information Systems
Data validity becomes a prominent research area in the context of data science driven research in the past years. In this study, we consider an application development on a cloud computing platform as a promising research area to examine digital trace data belonging to records of development activity undertaken. Trace data display such characteristics as found data that is not especially produced for research, event-based, and longitudinal, i.e., occurring over a period of time. Having these characteristics underlies many validity issues. We employ two application development trace data to articulate validity issues along with an iterative 4-phase research cycle. We demonstrate that when working with digital trace data, data validity issues must be addressed; otherwise it can lead to awry results of the research.
Citation - WoS: 19
Citation - Scopus: 31
A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data
(Mdpi, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk; Management Information Systems
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.