Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253

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  • Conference Object
    Secure Iot Entrance Using Mobile Application
    (Institute of Electrical and Electronics Engineers Inc., 2023) Abdulkareem, M.I.; Ashour, O.I.; Salman, Y.B.
    The impact of cyber security attacks increased globally, implementing proper security standards to protect data in the cyber environment has been prioritized, while assets are also at risk of being exploited through physical security breaches. Considering the potential risk of Radio Frequency Identification (RFID) cards being lost or stolen, third parties can effortlessly gain the privileges of an authorized person. In this paper, we propose a design for a secure access control system that operates on an Android phone with multi-factor authentication to enable secure access control systems. For this purpose, the authorized users are registered in a database that can be accessed by the access control system when the user authenticates his\her credentials with a login/password as well as biometric data from his\her smartphone. This system works when the user is 10 meters away from the access control system, after that the user will be asked to submit his\her biometric credentials. The database then compares the user's keys and grants access if authentication is successful. This design is intended to reduce the number of cases in which unauthorized access to a restricted area. The observed results clearly meet the required security of physical access control systems. © 2023 IEEE.
  • Conference Object
    Survey on Interface Usability Evaluation for Oil and Gas Critical Control Systems
    (Institute of Electrical and Electronics Engineers Inc., 2021) Alrawi, L.N.; Ashour, O.I.; Zeain, A.
    Usability is the key to develop and improve any system as it represents the direct contact point between users and machines. The use of the critical control system in the oil and gas industry is increasing. Due to the complexity of these systems, its interface usability should be assessed and developed periodically. In this research, the attributes that affect interface usability are identified. The usability of the Torque Turns System (TTS) is evaluated since the periods of downtime is projected to increase in the field. There are some works similar to our work however none of them had collected data directly from real operators from the field. An evaluation of the torque turn system interface usability is performed using questionnaire related to common interface usability attributes including accessibility, learnability, effectiveness, memorability, efficiency, safety, cognitive load, understandability, and satisfaction. The findings indicate a potential weakness in terms of understandability and accessibility © 2021 IEEE.
  • Conference Object
    Citation - Scopus: 3
    Spectrum Behavior Prediction and Optimized Throughput /Time Performance Using Ffnn in Cognitive Radio
    (Institute of Electrical and Electronics Engineers Inc., 2020) Sadeq, M.A.; Bayat, O.; Ilyas, M.; Ashour, O.I.
    Progressively, number of radio spectrum users is increasing as life tends towards new technologies in all sectors, so even those users of licensed band are demanding larger radio spectrum. Users may get assigned into other bands to balance the radio spectrum congestion. In this paper, radio spectrum is sensed for void detection and secondary user assignment. Cognitive users are participating the white band either by transmitting alongside with primary users or waiting until the hole is getting vacant. During the period of transmission, the behaviors of primary users are studied for determining the spectrum occupancy status. The activity of primary users is simulated as random variables due to uncertain behaviors from time perspectives. Issues like channel noise and fading effects stand as interrupters of spectrum sensing which make spectrum holes to appear busy due to such incidents. Cognitive Radio network is modeled by using MATLAB software so that both primary and secondary users can sense the spectrum and share the spectrum effectively by employing the approach of waiting time estimator which provides behaviors and activity matrix. Candidates are made to share the spectrum and hereafter transmission delay and throughput are examined when underlay and interweave spectrum sharing were in use. Three techniques are used to share the spectrum which are underlay, interweave and Feed Forward Neural Network. The results shown that feed forward neural network is outperformed in both time delay minimization and throughput enhancement. © 2020 IEEE.
  • Conference Object
    Citation - Scopus: 1
    Research Trends in Agile Software Development
    (Institute of Electrical and Electronics Engineers Inc., 2022) Tokdemir, G.; Uguz, S.
    Agile software development (ASD) is a popular research area that attracts the attention of the software development industry as well. Many studies have been conducted to explore the issues related to the ASD domain. Research is still very vigorous in this domain as there is continuous interest from companies to adopt ASD in their software development processes. Moreover, with the remote work setting that the pandemic forces, software companies search for new methods and approaches to manage their projects effectively and successfully. Hence, this study explores the research conducted in the last two decades in the ASD domain through text mining to reveal the trending topics that get attention from scholars. According to the results of this study, eight topics are identified for the research in ASD domain. Among those, topics of Team Communication, Agile Development, and Software Development Process are the most popular, the most funded, and the most cited topics respectively. © 2022 IEEE.
  • Conference Object
    Citation - Scopus: 1
    Lung Inflammatory Classification of Diseases Using X-Ray Images
    (Institute of Electrical and Electronics Engineers Inc., 2021) Mohanned, H.H.; Sürücü, S.; Choupani, R.
    Recently, studies in inflammatory diseases categorization become of interest in the research community, especially with the sudden outbreak of the Covid-19 virus. Transfer learning proved to be the state-of-the-art when it comes to image classification problems, or related tasks. These methods achieve good results in this type of applications. Lately, this pre-trained embedding became even popular due to X-ray related studies for early Covid-19 diagnosis. In this study, we investigate the X-ray image classification problem using the transfer learning method. We fine-tuned and trained our model using pre-trained models such as AlexNet, VGG16, DenseNet etc, and a baseline deep neural network. We then evaluated this model in terms of classification evaluation metrics. The study shows that DenseNet achieves high accuracy compared to the other pre-trained and baseline CNN models. © 2021 IEEE
  • Conference Object
    Citation - Scopus: 1
    Unmanned Surface Vehicle With Drown Map System
    (Institute of Electrical and Electronics Engineers Inc., 2019) Al-Dakheel, S.; Ozyer, S.T.; Can Ozdemir, F.; Karadag, A.; Al-Tekreeti, M.
    The drowning cases that are happened whether in sea or ocean specially increasing cases among refugees requires to employ the advanced technology and tools to encounter this problem. Internet of Things (IoT) and cloud computing techniques will be applied in marine sector to address the problems of drowning. Internet of things that represented in sensors, actuators...etc. generate a vast number of data that globally known as a Big data, due to the limited storage and processing of the physical units with the big number of data, a cloud computing is adopted to solve this problem. In this paper, an Unmanned Surface Vehicle (USV) contains GPS and Force Sensitive Resistor (FSR) sensors will be built to discover the location and approximate number of people exposed to drowning. In addition to USV, a real-time map system will be carried out to display this information. All the data and information that generating from the sensors and map system will be stored in a cloud in real-time. 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 IEEE.
  • Conference Object
    Citation - Scopus: 3
    Link Prediction in Knowledge Graphs With Numeric Triples Using Clustering
    (Institute of Electrical and Electronics Engineers Inc., 2020) Choupani, R.; Dogdu, E.; Bayrak, B.
    Knowledge graphs (KG) include large amounts of structured data in many different domains. Knowledge or information is captured by entities and relationships between them in KG. One of the open problems in knowledge graphs area is "link prediction", that is predicting new relationships or links between the given existing entities in KG. A recent approach in graph-based learning problems is "graph embedding", in which graphs are represented as low-dimensional vectors. Then, it is easier to make link predictions using these vector representations. We also use graph embedding for graph representations. A sub-problem of link prediction in KG is the link prediction in the presence of literal values, and specifically numeric values, on the receiving end of links. This is a harder problem because of the numeric literal values taking arbitrary values. For such entries link prediction models cannot work, because numeric entities are not embedded in the vector space. There are several studies in this area, but they are all complex approaches. In this study, we propose a novel approach for link prediction in KG in the presence of numerical values. To overcome the embedding problem of numeric values, we used a clustering approach for clustering these numerical values in a knowledge graph and then used the clusters for performing link prediction. Then we clustered the numerical values to enhance the prediction rates and evaluated our method on a part of Freebase knowledge graph, which includes entities, relations, and numerical literals. Test results show that a considerable increase in link prediction rate can be achieved in comparison to previous studies. © 2020 IEEE.