Novruz Allahverdi received the B.Sc. degree in computer engineering from Azerbaijan Technical University, Baku in 1972, and the Ph.D. degree in computer engineering from Moscow Power Engineering Institute (Technical University) in 1979. Since 1980, he has been an Assistant Professor, and then Associate Professor of Computer Science with the Department of Computer Engineering Azerbaijan Technical University. Since 1992, he began working for Selcuk University, Konya, Turkey where he became a professor and head of the Computer Engineering Department of the Faculty of Technology and then since 2015 he has been working at KTO Karatay University, Konya, Turkey, where he has been the head of Computer Engineering. At the present time, his scientific interests include applications of artificial intelligence in the different areas especially in the area design of fuzzy expert systems in the medical field, artificial neural networks applications, etc.
El presente capítulo analiza el rol de la onu en la resolución del conflicto armado nigeriano, a ... more El presente capítulo analiza el rol de la onu en la resolución del conflicto armado nigeriano, a partir del estudio del nacimiento y evolución de la organización terrorista Boko Haram y su impacto en la región. Para ello, se empleó una metodología de estudio de caso, con enfoque cualitativo y alcance explicativo. Dentro de los principales hallazgos de la investigación cabe destacar el papel limitado de la Organización en las etapas iniciales del conflicto, la internacionalización de este como parte del juego de poder entre las poten-cias del sistema internacional, y la preponderancia de los actores no estatales en la visibilización del drama humano ocasionado por las acciones del Estado Islámico en África Occidental. De allí, que se plantee la necesidad de reestructurar el Sistema de Naciones Unidas desde su misionalidad y alcance real, y replantear la lucha contra el terrorismo internacional desde un enfoque de derechos humanos.
Abstract: Within the fast development of knowledge and informatics, there are many new applicatio... more Abstract: Within the fast development of knowledge and informatics, there are many new applications and renovations in education field. The Distance Learning is one example of these renovations and applications. In this work, the development duration and application results of a distance learning application intended for master education were explained. This application consists of 4 courses for master education on Computer Education. These are Fuzzy Logic, Artificial Neural Networks, Expert Systems and Soft Computing Methods. The number of students and the successful rate of this application are shown as graphically in the last section.
Abstract: In this study a fuzzy expert system design for diagnosing, analyzing and learning purpo... more Abstract: In this study a fuzzy expert system design for diagnosing, analyzing and learning purpose of the prostate cancer diseases was described. For this process it was used prostate specific antigen (PSA), age and prostate volume (PV) as input parameters and prostate cancer risk (PCR) as output. This system allows determining if there is a need for the biopsy and it gives to user a range of the risk of the cancer diseases. There was observed that this system is rapid, economical, without risk than traditional diagnostic systems, has also a high reliability and can be used as learning system for medicine students.
In this study, a controlled fuzzy expert system (FES) was designed to provide the conditions nece... more In this study, a controlled fuzzy expert system (FES) was designed to provide the conditions necessary for operating rooms. For this purpose, real operating rooms have been studied to see if there are more useful, reliable and comfortable ones. How a operating room can be controlled with FES and its advantages and disadvantages have also been researched. For a theoretically visible FES to show system’s advantage a prototype operating room was built and a suitable configuration was designed. In this system, heat, particle, humidity and oxygen are used as input parameters, and fresh air entrance and the fan circulation are chosen as output parameters. With the help of an expert, appropriate language expressions and the membership function of these expressions were defined. The sensors were classified and sensor information was transferred to computer by means of an interface designed. To transfer the data to the system simultaneously, an interface was written in C#. Whether it provide...
2018 1st International Conference on Computer Applications & Information Security (ICCAIS), 2018
Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervi... more Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage DBN classification is proposed for achieving the efficiency of the DBN on arrhythmia disorders. Heartbeats from the MIT-BIH Arrhythmia database are classified into five groups which are recommended by AAMI. The Wavelet packet decomposition, higher order statistics, morphology and Discrete Fourier transform techniques were utilized to extract features. The classification performances of the DBN are 94.15%, 92.64%, and 93.38%, for accuracy, sensitivity, and selectivity, respectively.
IEEE Journal of Biomedical and Health Informatics, 2019
Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world.... more Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is to compare multiple machine-learning algorithms for the early diagnosis of COPD using multichannel lung sounds. Methods: Deep learning (DL) is an efficient machine-learning algorithm, which comprises unsupervised training to reduce optimization and supervised training by a feature-based distribution of classification parameters. This study focuses on analyzing multichannel lung sounds using statistical features of frequency modulations that are extracted using the Hilbert–Huang transform. Results: Deep-learning algorithm was used in the classification stage of the proposed model to separate the patients with COPD and healthy subjects. The proposed DL model with the Hilbert–Huang transform based statistical features was successful in achieving high classification performance rates of 93.67%, 91%, and 96.33% for accuracy, sensitivity, and specificity, respectively. Conclusion: The proposed computerized analysis of the multichannel lung sounds using DL algorithms provides a standardized assessment with high classification performance. Significance: Our study is a pioneer study that directly focuses on the lung sounds to separate COPD and non-COPD patients. Analyzing 12-channel lung sounds gives the advantages of assessing the entire lung obstructions.
Computer Methods and Programs in Biomedicine, 2016
Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart&... more Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD. The Hilbert-Huang transform (HHT), which is effective on non-linear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram data. The statistical features are extracted from instinct mode functions that are obtained applying the HHT to R-R intervals. Classification performance is examined with extracted statistical features using a multilayer perceptron neural network. The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively. Also, early diagnosis of the CHF was performed by interpretation of the CAD with a classification accuracy rate of 97.53%, specificity of 98.18% and sensitivity of 97.13%. As a result, a single system having the ability of both diagnosis and early diagnosis of CHF is performed by integrating the CAD diagnosis method to the CHF diagnosis method.
It has been aroused the necessity of extracting meaningful information from huge amount of availa... more It has been aroused the necessity of extracting meaningful information from huge amount of available data that is accumulated as result of development in computer technology and database software. Traditional methods can’t cope with turning the data to the knowledge due to amount and complexity of accumulated data that has so many hidden patterns in it. Thus, nowadays the data mining techniques are commonly used for analyzing huge amount of information. Classification, clustering and associated rule extraction of data mining techniques are preferred widely. Classification is the operation of determining class of the data by forming a model that makes use of data whose categories are previously determined. Data mining techniques are frequently used to form a classifier that determines belonging class of a new data among the predetermined classes. Although these classification methods including different classification and rule extraction algorithms are generally successful they don’t...
The ever-increasing spread of the use of the Internet in intelligence technologies has begun to r... more The ever-increasing spread of the use of the Internet in intelligence technologies has begun to revolutionise the entire economy (from production to commerce, tourism to publishing) and all phases of education; briefly, all aspects of life. As a consequence of the impact of rapid growth of use of the Internet on education, the idea of “Net-Aided Education” has become common and its application has increased. The fact that Net-Aided Education (NAE) has numerous advantages over conventional methods has impelled a number of educational institutions to reorganise accordingly. This research has been conducted using the course management system Moodle portal for Department of Electronics and Computer at Faculty of Technical Education of University of Selçuk, to compare NAE with the conventional method for the subject of Technical Drawing. Statistical analysis and evaluation of NAE and conventional methods have been carried out. Consequently, success rates of the group that learned using N...
An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormal... more An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multistage deep learning classification model for automatic arrhythmia classification. The proposed model includes a multi-stage classification system that uses ECG waveforms and the Second Order Difference Plot (SODP) features using a Deep Belief Network (DBN) classifier which has a greedy layer wise training with Restricted Boltzmann Machines algorithm. The multistage DBN model classified the MIT-BIH Arrhythmia Database heartbeats into 5 main groups defined by ANSI/AAMI standards. All ECG signals are filtered with median filters to remove the baseline wander. ECG waveforms were segmented from long-term ECG signals using a window with a length...
Expert Systems With Applications an International Journal, Jun 1, 2011
... Fast exact leave-one-out cross-validation of sparse least-squares support vector machines ...... more ... Fast exact leave-one-out cross-validation of sparse least-squares support vector machines ... However, using C-chart always experiences an excessive amount of false alarms, since control ... is performed within the model to accomplish scene interpretation and tracking improvement ...
El presente capítulo analiza el rol de la onu en la resolución del conflicto armado nigeriano, a ... more El presente capítulo analiza el rol de la onu en la resolución del conflicto armado nigeriano, a partir del estudio del nacimiento y evolución de la organización terrorista Boko Haram y su impacto en la región. Para ello, se empleó una metodología de estudio de caso, con enfoque cualitativo y alcance explicativo. Dentro de los principales hallazgos de la investigación cabe destacar el papel limitado de la Organización en las etapas iniciales del conflicto, la internacionalización de este como parte del juego de poder entre las poten-cias del sistema internacional, y la preponderancia de los actores no estatales en la visibilización del drama humano ocasionado por las acciones del Estado Islámico en África Occidental. De allí, que se plantee la necesidad de reestructurar el Sistema de Naciones Unidas desde su misionalidad y alcance real, y replantear la lucha contra el terrorismo internacional desde un enfoque de derechos humanos.
Abstract: Within the fast development of knowledge and informatics, there are many new applicatio... more Abstract: Within the fast development of knowledge and informatics, there are many new applications and renovations in education field. The Distance Learning is one example of these renovations and applications. In this work, the development duration and application results of a distance learning application intended for master education were explained. This application consists of 4 courses for master education on Computer Education. These are Fuzzy Logic, Artificial Neural Networks, Expert Systems and Soft Computing Methods. The number of students and the successful rate of this application are shown as graphically in the last section.
Abstract: In this study a fuzzy expert system design for diagnosing, analyzing and learning purpo... more Abstract: In this study a fuzzy expert system design for diagnosing, analyzing and learning purpose of the prostate cancer diseases was described. For this process it was used prostate specific antigen (PSA), age and prostate volume (PV) as input parameters and prostate cancer risk (PCR) as output. This system allows determining if there is a need for the biopsy and it gives to user a range of the risk of the cancer diseases. There was observed that this system is rapid, economical, without risk than traditional diagnostic systems, has also a high reliability and can be used as learning system for medicine students.
In this study, a controlled fuzzy expert system (FES) was designed to provide the conditions nece... more In this study, a controlled fuzzy expert system (FES) was designed to provide the conditions necessary for operating rooms. For this purpose, real operating rooms have been studied to see if there are more useful, reliable and comfortable ones. How a operating room can be controlled with FES and its advantages and disadvantages have also been researched. For a theoretically visible FES to show system’s advantage a prototype operating room was built and a suitable configuration was designed. In this system, heat, particle, humidity and oxygen are used as input parameters, and fresh air entrance and the fan circulation are chosen as output parameters. With the help of an expert, appropriate language expressions and the membership function of these expressions were defined. The sensors were classified and sensor information was transferred to computer by means of an interface designed. To transfer the data to the system simultaneously, an interface was written in C#. Whether it provide...
2018 1st International Conference on Computer Applications & Information Security (ICCAIS), 2018
Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervi... more Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage DBN classification is proposed for achieving the efficiency of the DBN on arrhythmia disorders. Heartbeats from the MIT-BIH Arrhythmia database are classified into five groups which are recommended by AAMI. The Wavelet packet decomposition, higher order statistics, morphology and Discrete Fourier transform techniques were utilized to extract features. The classification performances of the DBN are 94.15%, 92.64%, and 93.38%, for accuracy, sensitivity, and selectivity, respectively.
IEEE Journal of Biomedical and Health Informatics, 2019
Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world.... more Goal: Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases in the world. Because COPD is an incurable disease and requires considerable time to be diagnosed even by an experienced specialist, it becomes important to provide analysis abnormalities in simple ways. The aim of the study is to compare multiple machine-learning algorithms for the early diagnosis of COPD using multichannel lung sounds. Methods: Deep learning (DL) is an efficient machine-learning algorithm, which comprises unsupervised training to reduce optimization and supervised training by a feature-based distribution of classification parameters. This study focuses on analyzing multichannel lung sounds using statistical features of frequency modulations that are extracted using the Hilbert–Huang transform. Results: Deep-learning algorithm was used in the classification stage of the proposed model to separate the patients with COPD and healthy subjects. The proposed DL model with the Hilbert–Huang transform based statistical features was successful in achieving high classification performance rates of 93.67%, 91%, and 96.33% for accuracy, sensitivity, and specificity, respectively. Conclusion: The proposed computerized analysis of the multichannel lung sounds using DL algorithms provides a standardized assessment with high classification performance. Significance: Our study is a pioneer study that directly focuses on the lung sounds to separate COPD and non-COPD patients. Analyzing 12-channel lung sounds gives the advantages of assessing the entire lung obstructions.
Computer Methods and Programs in Biomedicine, 2016
Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart&... more Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD. The Hilbert-Huang transform (HHT), which is effective on non-linear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram data. The statistical features are extracted from instinct mode functions that are obtained applying the HHT to R-R intervals. Classification performance is examined with extracted statistical features using a multilayer perceptron neural network. The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively. Also, early diagnosis of the CHF was performed by interpretation of the CAD with a classification accuracy rate of 97.53%, specificity of 98.18% and sensitivity of 97.13%. As a result, a single system having the ability of both diagnosis and early diagnosis of CHF is performed by integrating the CAD diagnosis method to the CHF diagnosis method.
It has been aroused the necessity of extracting meaningful information from huge amount of availa... more It has been aroused the necessity of extracting meaningful information from huge amount of available data that is accumulated as result of development in computer technology and database software. Traditional methods can’t cope with turning the data to the knowledge due to amount and complexity of accumulated data that has so many hidden patterns in it. Thus, nowadays the data mining techniques are commonly used for analyzing huge amount of information. Classification, clustering and associated rule extraction of data mining techniques are preferred widely. Classification is the operation of determining class of the data by forming a model that makes use of data whose categories are previously determined. Data mining techniques are frequently used to form a classifier that determines belonging class of a new data among the predetermined classes. Although these classification methods including different classification and rule extraction algorithms are generally successful they don’t...
The ever-increasing spread of the use of the Internet in intelligence technologies has begun to r... more The ever-increasing spread of the use of the Internet in intelligence technologies has begun to revolutionise the entire economy (from production to commerce, tourism to publishing) and all phases of education; briefly, all aspects of life. As a consequence of the impact of rapid growth of use of the Internet on education, the idea of “Net-Aided Education” has become common and its application has increased. The fact that Net-Aided Education (NAE) has numerous advantages over conventional methods has impelled a number of educational institutions to reorganise accordingly. This research has been conducted using the course management system Moodle portal for Department of Electronics and Computer at Faculty of Technical Education of University of Selçuk, to compare NAE with the conventional method for the subject of Technical Drawing. Statistical analysis and evaluation of NAE and conventional methods have been carried out. Consequently, success rates of the group that learned using N...
An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormal... more An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multistage deep learning classification model for automatic arrhythmia classification. The proposed model includes a multi-stage classification system that uses ECG waveforms and the Second Order Difference Plot (SODP) features using a Deep Belief Network (DBN) classifier which has a greedy layer wise training with Restricted Boltzmann Machines algorithm. The multistage DBN model classified the MIT-BIH Arrhythmia Database heartbeats into 5 main groups defined by ANSI/AAMI standards. All ECG signals are filtered with median filters to remove the baseline wander. ECG waveforms were segmented from long-term ECG signals using a window with a length...
Expert Systems With Applications an International Journal, Jun 1, 2011
... Fast exact leave-one-out cross-validation of sparse least-squares support vector machines ...... more ... Fast exact leave-one-out cross-validation of sparse least-squares support vector machines ... However, using C-chart always experiences an excessive amount of false alarms, since control ... is performed within the model to accomplish scene interpretation and tracking improvement ...
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