Browsing by Author "Cakar, Mehmet Akif"

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Magnetic-particle based signal amplification method integrated with mobile-devices for low cost biosensing
(ELSEVIER SCIENCE BV, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, 2017) Mzava, Omary; Tas, Zehra; Lafci, Vahit Can; Cakar, Mehmet Akif; Ozdur, Ibrahim; Icoz, Kutay; AGÜ, Mühendislik Fakültesi, Mühendislik Bilimleri Bölümü;
We present a signal amplification method for biosensing applications using magnetic particles. In this method, mobile devices and simple spherical glass beads are used as a low-cost microscope to detect magnetic particles. Magnetic particles have two main functions; 1) conventionally capture, separate and transport target molecules 2) form magnetic dipoles under an applied external magnetic field to attract other magnetized particles. When magnetic particles accumulate and form a cluster, the corresponding pixel area in the image taken by the simple microscope is increased resulting in signal amplification. Current focus of new generation biosensor research is to increase the sensitivity levels of the devices to compete with current lab analysis tools while inherently having other advantages such as being low-cost, portable and simple. Biosensors based on micro/nano magnetic particles use various measurement techniques and amplification methods. In order to fully benefit from the advantages of micro/nano technology based systems, measurement set up must be also portable and have high sensitivity. Mobile devices and applications are taking place in medical fields and have high potential for future. In this work mobile devices are employed as measurement setups for the magnetic particle based sensing and signal amplification. The amplification method is not based on bimolecular binding thus cost efficient. After the images of the magnetic particles are taken, these images are sent to cloud computing for analysis by the mobile device. Matlab codes run on cloud servers for processing the images. Finally results are received and displayed on the mobile device. The mobile device based imaging system is able to detect 7 mu m size particles within a 1500 mu m x1500 mu m area and magnetic bead accumulation resulted in at least 5-fold signal amplification. The applied magnetic field is approximately 15 mT and the cost of the system excluding mobile device is under 20 cents. The method is promising for immunomagnetic bead assisted biosensors. (c) 2016 The Authors. Published by Elsevier Ltd.
Using Students' Performance to Improve Ontologies for Intelligent E-Learning System
(EDAM, KISIKLI MH ALEMDAG CD YAN YOL SK, SBK IS MERKEZI NO 5, KAT 1 USKUDAR, ISTANBUL, 81190, TURKEY, 2015) Sanalan, Vehbi A.; Cakar, Mehmet Akif; Ozdemir, Esra Benli; Kaya, Sukru; Icoz, Kutay; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü;
Ontologies have often been recommended for E-learning systems, but few efforts have successfully incorporated student data to represent knowledge conceptualizations. Defining key concepts and their relations between each other establishes the backbone of our E-learning system. The system guides an individual student through his/her course by evaluating their progress and suggesting instructional material to review based upon their answers. Three main tasks are performed within this framework: building ontologies for the course, measuring a student's understanding level for the concepts, and making personal suggestions to create an individualized learning environment. This paper presents: the integration of ontologies, assisted with student data, together with an intelligent Recommendation Module for the development of an E-learning system; the comparison and correction adaption of ontology from students' mind maps; and the assessment of students' actual weaknesses in comparison to what Recommendation Module suggests. The sample of 127 students, five classrooms, was conveniently selected among seventh grade students of a demographically average school in a major city in Turkey. The students' achievement was assessed and the scores for different questions were investigated for associations with concepts made in the students' minds. The results provided significant correlations among scores, and a fit model for the concepts represented by questions. The student suggested model slightly differed from the ontology map from the experts. Based on the data-supported model, the Recommendation Module more accurately determined the students' learning deficiencies and suggested concepts to be reviewed.