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

ORCID ID

0000-0002-3995-6566

Scopus Author ID

8873732700

Full item page

Scholarly Output

50

Articles

21

Citation Count

127

Supervised Theses

14 Scholarly Output Search Results

Now showing 1 - 10 of 50

Citation Count: 24
A 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.
Citation Count: 18
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; Öğrenci, Arif Selçuk
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.
Citation Count: 4
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; Aydin, Mehmet Nafiz
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 Count: 0
Analysis and Implications of the Giant Component for an Online Interactive Platform
(Int Business Information Management Assoc-IBIMA, 2016) Aydın, Mehmet Nafiz; Perdahci, N. Ziya
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 Count: 13
Adoption 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, Gizdem
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 Count: 5
A Country-Specific Analysis on Internet Interconnection Ecosystems
(IEEE, 2018) Cakmak, Gorkem; Aydın, Mehmet Nafiz
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.
Proposing a Model for Precision Management Supervised With Machine Learning in Livestock Management
(Kadir Has Üniversitesi, 2021) Ödevci, Bahadır Baran; Emsen, Ebru; Aydın, Mehmet Nafiz
The global demand for meat is predicted to rise by 40% in the next 15 years, owing to an increase in the number of people adopting protein-richer diets, and technology solutions in agricultural and livestock production systems are likely to play a vital role in addressing this issue. On the other hand, while expanding meat output, it will be critical to discover ways to reduce livestock farming's environmental footprint and assure high levels of animal care and health. In this thesis, we aim to propose a model and approach along with a number of steps to follow for a livestock farm to adapt an information management system to attain optimum production efficiency. We are seeking answers to respond to the following research question: How can a livestock farm utilize information management systems for optimum efficiency? In order to expand the research on a specific livestock case study, we focus on intensively managed sheep for lamb production. However, the model and approach proposed in this thesis can be applicable to any livestock farming that aims to utilize information systems for precision management of farm operations. First, we reviewed scientific research related to long-standing, novel-technology, and data sensors with emphasis on data-information-knowledge-wisdom and decision-making processes and for intensively managed sheep for lamb production. Secondly, we addressed what data elements exist in the context of a livestock farm and how data elements in the context of livestock farms are associated. Special attention was given to the data model of the farm context for managerial precision livestock farming (PLF) systems. Thirdly, we proposed the decision-making points supervised by machine learning models in a PLF management information system for intensively managed sheep for lamb production. At this point, we developed and adapted a Mobile Sheep Manager Software (M-SMS) for a commercial lamb production model using an appropriate cloud architecture that collects and utilizes farm data and responds to the farm management with respect to insights into the operational and financial aspects of the farm. The technology identifies real-time alarms pertaining to animal welfare, health, environmental effects, and production on the farm and provides troubleshooting recommendations. We also looked at its suitability for user experience as well as its impact on farm profitability and sustainability. This research has shown that M-SMS combined with cloud services compounded with Predictive Analytics Services can fine-tune flock management and significantly improve operational excellence. According to the usability results, intensive sheep farmers had access to "point and click" solutions to keep legislative records, attain operational guidance and build flock performance data. Finally, we propose a model and steps to follow to adapt the information management system to any livestock management system in order to attain optimum efficiency. It was concluded that the architecture of this application can be easily adapted to other intensively managed livestock if the steps in this study are followed precisely.
Citation Count: 0
Understanding Virtual Onboarding Dynamics and Developer Turnover Intention in the Era of Pandemic
(Elsevier Science inc, 2024) Akdur, Gorkem; Aydin, Mehmet N.; Akdur, Gizdem
This study examines the dynamics of virtual onboarding (VO) for Salesforce Commerce Cloud developers during the COVID-19 pandemic in a multinational software company. The newly developed Virtual Integration and Retention Framework (VIRF), which provides an improved understanding of VO, customized to the opportunities and challenges presented by the pandemic, is the fundamental concept of this study. A two-staged, higher-order constructed (HOC) quantitative research approach was used for the study, revealing a negative relationship between VO success and the challenges brought on by the pandemic. This emphasizes how difficult it can be to transition to remote work settings, especially regarding how operational effectiveness and employee well-being interact. Furthermore, the study demonstrates the positive connection between VO success and the delivery of technology and equipment during the pandemic. This result emphasizes how important logistical support is to the effectiveness of remote work arrangements. The study's key findings show positive impact of successful VO on developers' job satisfaction and workplace relationship quality (WRQ). Strong VO practices are essential to improve employee retention, as evidenced by the inverse correlation between these factors and turnover intentions. The study uses mediation analysis, with job satisfaction and WRQ acting as mediators, to further clarify how VO success influences turnover intentions. This study offers an in-depth understanding of VO practices during the pandemic. It discusses the future of remote work and onboarding procedures while navigating the immediate difficulties caused by the outbreak. The study emphasizes how important VO is for improving WRQ, decreasing turnover intentions of developers within the software company, and improving job satisfaction. These insights benefit organizations trying to improve developer integration and retention in changing work environments and improve their remote work strategies.
Social Network Analysis of Innovation Mentor Community of Practice
(Kadir Has Üniversitesi, 2022) ALTINIŞIK, Gunda Esra; Aydın, Mehmet Nafiz
Innovation is directly related to the development of economies, and with the awareness of its criticality, various nation-wide support programs and innovation communities have emerged in recent years. These communities are established along their own specific structures and dynamics that can be examined by their level of connectedness and its underlying members’ attributes. In this research, a government-sponsored innovation mentors’ community of practice (CoP) has been examined. Thus, the members are advised to bring their knowledge to adopt the framework to specific cases and share their experiences with their peers. A CoP stands on the basic premise that the practice (knowhow) is shared among members and stimulates connectedness along their competencies. In this context, the first question is: how to measure the connectedness of the community and whether the CoP under investigation achieves the desired level of connectedness? The second is: what specific mentors’ attributes (competencies) characterize the preferred choices of connectedness? More particularly, how knowledge-sharing preferences are associated by the mentors’ attributes of this CoP? We employed Social Network Analysis techniques and Jaccard Similarity to answer them. The findings reveal that the CoP of innovation mentors is highly connected for a giant component, but low at the network level. Degree, title and institution as the members’ attributes may not play a significant role in the connectedness of this community. Even though mentors meet on a denominator in basic competencies in their cooperation, the findings show that they cooperate interdisciplinary. We argue that the dissimilar competencies of the connected mentors can be considered as a signature of the very idea of connectedness. Further research is needed to validate this claim with richer data, preferably with a temporal aspect.
A Network Science Approach To Correlations Between Course Achievement and Community Structure in School Friendship Networks
(Kadir Has Üniversitesi, 2017) Kafkas, Kenan; Aydın, Mehmet Nafiz
in this research we examine a secondary school social network. We apply social network analysis (SNA) techniques on the close friendship structure of the students. Our aim is to answer the following questions regarding the network. The first question is what are the mixing values with respect to test achievement scores gender and class. The second question is how are the communities in the network structured. Such findings can be significant assets in understanding and improving the learning environment. They may be used to help teachers and school managers in deciding more workable and efficient student matchings. For this study we conducted a survey to a group of 10th grade students and gathered the necessary information to construct the social network around the school. Our findings show that the friendship in overall network is neither assortative nor disassortative with respect to academic success in other words the two attributes are not correlated. On the other hand gender and class mixing measures are significantly high which not surprisingly suggests that the students prefer to bond with their classmates and also with the same gender friends. Finally after examining the communities within each classroom we observe similar community structures. in the light of these findings we propose a method for composing the classrooms to construct an efficient and successful learning environment.