– The purpose of this paper is to investigate the factors that affect the adoption of cloud computing by firms belonging to the high‐tech industry. The eight factors examined in this study are relative advantage, complexity, compatibility, top management support, firm size, technology readiness, competitive pressure, and trading partner pressure., – A questionnaire‐based survey was used to collect data from 111 firms belonging to the high‐tech industry in Taiwan. Relevant hypotheses were derived and tested by logistic regression analysis., – The findings revealed that relative advantage, top management support, firm size, competitive pressure, and trading partner pressure characteristics have a significant effect on the adoption of cloud computing., – The research was conducted in the high‐tech industry, which may limit the generalisability of the findings., – The findings offer cloud computing service providers with a better understanding of what affects cloud computing adoption characteristics, with relevant insight on current promotions., – The research contributes to the application of new technology cloud computing adoption in the high‐tech industry through the use of a wide range of variables. The findings also help firms consider their information technologies investments when implementing cloud computing.

Understanding the determinants of cloud computing adoption

A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing

Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

Progress in Outlier Detection Techniques: A Survey

We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 refereed journal articles published since the inception of cloud computing research. The articles are classified based on a scheme that consists of four main categories: technological issues, business issues, domains and applications, and conceptualising cloud computing. The results show that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging. This review provides a reference source and classification scheme for IS researchers interested in cloud computing, and to indicate under-researched areas as well as future directions.

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A Descriptive Literature Review and Classification of Cloud Computing Research

In this review, the applications of molecularly imprinted polymer (MIP) materials in the area of electrochemical sensors have been explored. The designs of the MIPs containing different polymers, their preparation and their immobilization on the transducer surface have been discussed. Further, the employment of various transducers containing the MIPs based on different electrochemical techniques for determining analytes has been assessed. In addition, the general protocols for getting the electrochemical signal based on the binding ability of analyte with the MIPs have been given. The review ends with describing scope and limitations of the above electrochemical based MIP sensors.

Molecularly Imprinted Electrochemical Sensors

Internet, as a marketing medium in the tourism industry, is a very valuable destination marketing tool. Different tourism authorities and organizations serve as marketing and service providers. They benefit from the use of Internet within the concept of marketing efforts. A new technology, Semantic Web, brought incremental changes by bringing machine-readable descriptions to current web interfaces of these organizations. This paper provides necessary framework for the applicability of Semantic Web technology on Destination Marketing. It creates a methodology on how web pages are created and analyzed with the value-added tourism marketing purposes. Semantic Web technology achieves such interoperability by referring to ontology models. The ontology presented here determines the value of this integration in destination marketing organizations.

OWL Ontology Development for Destination Marketing

Manipulators serve worldwide to increase quality requirements and speed up the production of the manufacturing industry. Typical applications of manipulators include welding, painting, ironing, assembly, picking and placing, product inspection. In this study, we focus on designing and implementing a low-cost training manipulator set for "Robotic and Control Laboratory" in the University. The set includes two 6-DOF manipulators controlled in coordination by Raspberry PI computer using Codesys platform. Manipulators can move to the desired coordinates through forward kinematic calculations The link transformation matrices for each joint are obtained by the Denavit-Hartenberg (DH) conventions. Due to complexity of transformation matrices, Matlab program is used to solve the matrix equations and the calculation results are transferred to Codesys environment. Codesys control application creates a virtual PLC functionality for Raspberry PI that communicates with servo control board by I2C communication protocol. Servo control board generates Pulse Width Modulation (PWM) signals applied to servo driver motors for obtaining the desired movements. In order to ease the user-manipulator interactions, a web-based interface is developed for running on a tablet PC or smart phones. Students use the set successfully for various practices in the laboratory.

A Low Cost Manipulator Training Set

The concept of the Internet of Industrial Things (IIoT), which emerged when the Internet of Things (IoT) applications started to be used in the industry, has provided the efficiency, flexibility, traceability and security to the end users in their workspaces. Indoor monitoring and control systems, which are the leading applications of the Internet of industrial things, play a major role in providing these elements. Over the years, these systems have evolved from wired alarm systems to data loggers, wireless sensor networks, and then to with cloud computing based smart gateways. In the face of this evolution of systems, the expectations of end users such as being able to control at tens of thousands of points, high quality of service or achieving commercial targets have increased at the same rate. In order to meet all these expectations, a modular and compact platform based on Fog Computing and single board computer, which provides optimum performance and cost, has been designed and developed in this study. When the results obtained from the test processes implemented under real scenarios are examined, it is seen that the hardware used and the software infrastructure, all open source, are sufficient and usable for indoor monitoring and control applications.

Design and Implementation of a Customized Micro Server Platform for Indoor Monitoring and Control

In Turkey, the profession orientation programs for elementary education students have a critical importance. In the aspect of profession orientation application, the least dealt population is unfortunately the elementary school students. In this study, the problems caused by insufficient orientation and guidance of profession for those students are investigated and a web based software is developed to reduce these problems as much as possible. Mentality fields are determined in accordance with Multi-Intelligence theory and recommendations are presented to students in order to get more achievement for profession selection using a software developed for this purpose.

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Web Based Profession Orientation in Elementary Education

Academic and industrial studies on smart systems, which have entered all areas of our lives, continue to their rapid developments along with gaining momentum with Industry 4.0. Especially, many of the different production devices with their molding, printing, shaping, and cutting capabilities have a certain level of automation. They work together with the material information they process, and the compatibility with other machines to make the whole production system work effectively and properly. While the abundance of data acquired is an important source for better analytics, obtaining information for business purposes from this data and helping decision support systems is the most important task expected from Information Technology and Systems in the organization. In this paper, we propose an FPGA-based edge information infrastructure to evaluate critical data from the production devices, distributed sensors, and other ISs in any industrial environment to increase the utilization and performance of the total machinery. This study helps the predictive maintenance decision for a sample plastic injection molding device according to our industrial scenario. A sample data set downloaded from the Internet with the factors like speed, vibration, and the temperature was used. An FPGA (Field Programmable Gate Array) design that will run the necessary ML algorithms with the sensor data and existing information system inputs (ERP, MES) has been carried out by using Xilinx Design Tools and Vitis IDE 2020.2. In this study, the ANFIS (Adaptive Network-Based Fuzzy Inference System) system, which is an approach consisting of the integration of artificial neural networks and Fuzzy Logic, has been chosen as an Artificial Intelligence application. The estimation results obtained were evaluated over the accuracy rates achieved in similar studies in the literature.

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Tuncay Ercan

Papers

OWL Ontology Development for Destination Marketing

A Low Cost Manipulator Training Set

Design and Implementation of a Customized Micro Server Platform for Indoor Monitoring and Control

Web Based Profession Orientation in Elementary Education

Importance of Edge Computing in Critical Manufacturing Systems: FPGA Implementation