Rahib Abiyev

Dr. Rahib Abiyev

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Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2018 in Journal of Healthcare Engineering
Verified
Journal Article
Open access
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RARahib Abiyev
Rahib Abiyev
MMMohammad Ma'aitah
Mohammad Ma'aitah
Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.
13 June 2020 in Applied Sciences
Verified
Journal Article
Open access
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RARahib Abiyev
Rahib Abiyev
MAMurat Arslan
Murat Arslan
JIJohn Bush Idoko
John Bush Idoko
Boran ŞEKEROĞLUBoran ŞEKEROĞLU
Boran ŞEKEROĞLU
AIAhmet Ilhan
Ahmet Ilhan
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.
18 November 2021 in Applied Sciences
Verified
Journal Article
Open access
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Boran ŞEKEROĞLUBoran ŞEKEROĞLU
Boran ŞEKEROĞLU
RARahib Abiyev
Rahib Abiyev
AIAhmet Ilhan
Ahmet Ilhan
MAMurat Arslan
Murat Arslan
JIJohn Bush Idoko
John Bush Idoko
Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented.
28 May 2018 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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Journal Article
Closed access
RARahib Abiyev
Rahib Abiyev
NANurullah Akkaya
Nurullah Akkaya
IGIrfan Gunsel
Irfan Gunsel
The control of mobile robots in an uncertain environment that is densely cluttered with dynamic obstacles is an important problem in robotics. A fuzzy set theory is one of the important tools for describing uncertainty and designing control system of the mobile robot. In this paper, a Z-number-based fuzzy inference system for control of the omnidirectional soccer robot is proposed. To design the fuzzy control system of the robot, at first stage, the modeling of the kinematics and dynamics of the omnidirectional four-wheel-driven soccer robot was carried out. Then the design of control system is implemented for the linear and angular speed. Using Z-number-based fuzzy rule base and interpolative reasoning the Z-number-based fuzzy inference system is proposed. The proposed Z-number-based inference mechanism is then used for control of the omnidirectional robot. The fuzzy controller algorithm has been tested in simulations and provided satisfactory results at runtime. The proposed control algorithm minimizes the deviation between the current plant output signal and the reference signal. The results obtained in this paper demonstrate the suitability of using Z-number-based system in control of soccer robots.
29 September 2016 in BioMed Research International
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Journal Article
Open access
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RARahib Abiyev
Rahib Abiyev
NANurullah Akkaya
Nurullah Akkaya
EAErsin Aytac
Ersin Aytac
IGIrfan Günsel
Irfan Günsel
AÇAhmet Çağman
Ahmet Çağman
The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.
29 January 2022 in International Journal of Computer Assisted Radiology and Surgery
Verified
Journal Article
Closed access
PDF file
AIAhmet Ilhan
Ahmet Ilhan
Boran ŞEKEROĞLUBoran ŞEKEROĞLU
Boran ŞEKEROĞLU
RARahib Abiyev
Rahib Abiyev