Yazar "Temiz, Mustafa" seçeneğine göre listele

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    A Study On Profiling Students via Data Mining
    (2019) Alan, Mehmet Ali; Temiz, Mustafa
    Data mining is a significant method which is utilized in order to reveal the hidden patterns and connections within big data. Themethod is used at various fields such as financial transactions, banking, education, health sector, logistics and security. Eventhough analysis towards the consumption habits of the customers is carried out via association rules mining more often, whichis one of the basic methods of data mining, the method is also utilized in order to profile patients and students. As well as thecustomization of a customer is of high significance, so is distinguishing and customizing a student. Within this study, studentswere tried to be profiled via data mining of the student data of a high school. A set of qualities, that can directly affect theperformance of students such as health conditions, financial resources, life standards and education level of the families, weretaken into consideration. For that purpose, upon the analysis of data of 443 students in the database, a data warehouse wasestablished. The Apriori algorithm, which is one of the popular algorithms of association rules mining, is utilized for the dataanalysis. Apriori algorithm was able to produce 72 rules which are accurate above 90%. It is thought that the produced rules canbe of help in profiling the students, and they can contribute to work of school management, teachers, parents and students.
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    Automatic Classification of Natural Stone Tiles with Computer Vision
    (IEEE, 2018) Kaynar, Oguz; Torun, Yunis; Temiz, Mustafa; Gormez, Yasin
    Classification in natural stone industry have a great importance for enterprises. There are reinstatement cases arising from the fact that ordered granite parties are not the same as the agreed sample parties at the beginning, which causes significant economic losses for the companies. There is a greater need to classify tiles using computer-aided image processing methods for the development of quality control processes that have become increasingly important due to the rapidly increasing competition and globalization in the natural stone industry. In this type of automatic systems, the attributes that give information about color and surface are extracted from the images of natural stone tiles with image processing techniques and then the data set obtained by using these attributes are classified by various artificial intelligence and data mining techniques. In this study, a classification was made on a dataset consisting of 996 pictures of natural stone tiles from six categories obtained from a natural stone producer (Beta Mermer I. C.) operating in Sivas. Gray level co-occurrence matrix (GLCM) and local binary pattern (LPB) are used to obtain pattern information of granite tiles. Several statistics related to each color channel were used to obtain color information of granites. Various datasets are created using only pattern information and combination of pattern and color information of tiles. Subsequently, classification performance of these datasets are compared using several algorithms such as, artificial neural networks, support vector machines, and naive bayes.
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    Blockchain Based User Management System
    (IEEE, 2020) Temiz, Mustafa; Soran, Ahmet; Arslan, Halil; Erel, Hilal
    Blockchain is a reliable and transparent structure formed by distributing the data in blocks connected to each other using various cryptography techniques to other points on the network. The difference from the existing database operations is that the authorities and responsibilities do not exist in a single central authority, and that these powers and responsibilities are distributed to the other nodes in the network and the assignment is shared. To provide this, peer to peer network infrastructure is used. However, at this stage, authentication in terms of security is one of the basic security mechanisms. In this study, a user management system which can be integrated with more reliable and current technologies, which is thought to be the solution to speed problems in blockchain, is proposed.
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    Machine Learning and Text Mining based Real-Time Semi-Autonomous Staff Assignment System
    (Comsis Consortium, 2024) Arslan, Halil; Emreis, Yunus; Gormez, Yasin; Temiz, Mustafa
    The growing demand for information systems has significantly increased the workload of consulting and software development firms, requiring them to manage multiple projects simultaneously. Usually, these firms rely on a shared pool of staff to carry out multiple projects that require different skills and expertise. However, since the number of employees is limited, the assignment of staff to projects should be carefully decided to increase the efficiency in job -sharing. Therefore, assigning tasks to the most appropriate personnel is one of the challenges of multiproject management. Assigning a staff to the project by team leaders or researchers is a very demanding process. For this reason, researchers are working on automatic assignment, but most of these studies are done using historical data. It is of great importance for companies that personnel assignment systems work with real-time data. However, a model designed with historical data has the risk of getting unsuccessful results in real-time data. In this study, unlike the literature, a machine learning -based decision support system that works with real-time data is proposed. The proposed system analyses the description of newly requested tasks using textmining and machine -learning approaches and then, predicts the optimal available staff that meets the needs of the project task. Moreover, personnel qualifications are iteratively updated after each completed task, ensuring up-to-date information on staff capabilities. In addition, because our system was developed as a microservice architecture, it can be easily integrated into companies' existing enterprise resource planning (ERP) or portal systems. In a real -world implementation at Detaysoft, the system demonstrated high assignment accuracy, achieving up to 80% accuracy in matching tasks with appropriate personnel.