Bilgisayar Mühendisliği Bölümü

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Citation - Scopus: 2
Application of Artificial Intelligence in Early-Stage Diagnosis of Sepsis
(Association for Computing Machinery, 2022) Sezer, E.A.; Sever, H.; Par, O.E.
Patient care is a critical task, which requires a lot of effort. Medical practitioners face many challenges, especially during diagnosing different diseases. Sepsis is one of the riskiest diseases, which proves to be lethal for Intensive Care Unit (ICU) patients. World Health Organization (WHO) has declared it a major cause of death worldwide. Early-stage diagnosis of sepsis can help in terminating it in the start. But unfortunately, medical practitioners encounter hitches in the early-stage diagnosis of sepsis. The study used SOFA (Sequential Organ Failure Assessment) for measuring the severity of sepsis in patients. The study employs artificial intelligence techniques such as Multilayer Perceptron (MLP) and Random Forest (RF) to diagnose early-stage of sepsis. The study compared the performance of MLP (connected and non-connected) and Random Forest (connected and non-connected) algorithms. The results indicate that for both of the algorithms, the connected method yielded better results than the non-connected method. Further, it was found that RF both connected and non-connected algorithms yielded better results than MLP algorithms and the Random Forest connected algorithm yielded highly accurate results for diagnosing early-stage sepsis in the 3rd hour. © 2022 ACM.
Citation - Scopus: 1
Comparative Analysis of Machine Learning Techniques Using Customer Feedback Reviews of Oil and Gas Companies
(Association for Computing Machinery, 2020) Alrawi, L.N.; Ashour, O.I.A.
Sentiment analysis is the process of computationally identifying and categorizing opinions from a piece of text to determine whether the writer's attitude towards a practical topic, products or services is positive, negative or neutral. In this study, Machine Learning techniques are used to perform sentiment analysis on Oil and Gas customer feedback data. We present a comparison of different classification algorithms used for opinion mining, including Support Vector Machine (SVM), Naïve Bayes (NB), Instance Based Learning (IB3), Random Forest (RF), Partial Decision trees (PART), and Logit Boost (LB). Many studies have been performed on sentiment analysis in different sectors, but research into Oil and Gas customer feedback has been limited. Therefore, we have targeted a pathless sector, namely the Petroleum sector, where companies express their opinions towards specific products or services. Waikato Environment for Knowledge Analysis (WEKA) is used for experimental results. The WEKA environment is open source software entailing a collection of machine learning algorithms to solve data mining problems. The main aim of this study is to evaluate the efficiency of the above mentioned classifiers in terms of Precision, Recall, F-Measure and Accuracy. The findings of the comparison analysis indicate that the Naïve-Bayes classifier gives the best Accuracy of all classifiers. A small dataset could be considered as a limitation to our study due to the difficulty of gaining more datasets at the time of the research. However, this research will play a vital role for researchers in making decisions about the algorithm that they are going to use to solve their data mining problems. © 2020 ACM.
Unmanned Surface Vehicle Prototype With Obstacle Avoidance System Area: Applications and Evaluation of Real-Time Big Data Systems
(Association for Computing Machinery, 2019) Al-Tekreeti, M.; Özyer, S.T.; Özdemir, C.; Karadag, A.; Al-Dakheel, S.
In this paper, Unmanned Surface Vehicles (USV) prototype and system design have been presented for the rescue of human life at sea. The USV will be protected from obstacles that may cause a crash to the USV by avoiding the obstacles using many kinds of detection sensors. All of these sensors send results to Crash Avoidance System (CAS) and to the main computer to control the USV direction depends on the obstacle shape, size or if it is a moving obstacle or not. The sensors that will be used for this purpose are Light Detection and Ranging (LIDAR) sensor, LIDAR-Lite sensors and ultrasonic sensors. All the information that will be collected from all these types of sensors will be used to direct the USV to the safe path. This work is a part of the research and development project which is accepted in Turkey Government with the collaboration of the University and Industry. © 2019 ACM.
Citation - Scopus: 2
Web Application Based on Mvc Laravel Architecture for Online Shops
(Association for Computing Machinery, 2020) Mahmood, M.T.; Ashour, O.I.A.
Working with traditional methods to develop a web application has great limitations, is very time-consuming and can lead to a number of unexpected errors. Therefore, a new technology, namely MVC pattern frameworks, was found by some companies to deal with such issues. In this paper, we present a design and implementation for a web-based application for e-commercial shops and third-parties to buy products from online shops using the Laravel framework. As result of our research, we were able to determine that the development was standardized and non-business logic relationships were automatically processed. Moreover, there was much scalability, which provided us with more efficiency through the implementations. © 2020 ACM.

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