M. Ali Akcayol
Department of Computer Engineering, Gazi University
akcayol@gazi.edu.tr,  maakcayol@gmail.com

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SHORT BIO (Curriculum vitae)
He received BS, MS and PhD degrees from Gazi University, 1993, 1998 and 2002, respectively. In his dissertation, he worked on applying fuzzy logic and artificial neural networks to dynamic systems.

He has been at Michigan State University, USA, 2004, for postdoctoral research.

He is currently director of Big Data and Mobile Systems Laboratory that receives both national and international funding. The projects that he was principal investigator have been funded by various public and private sector organizations including Gazi University, TÜBİTAK, Ministry of Industry and Technology, TAI and Huawei.

He has served as editor/field editor and reviewer for numerous international and national journals. He has served as session chairman, member of the organizing committee and member of the scientific committee at prestigious conferences around the world. He has ACM professional membership.

His research interests include, artificial intelligence, deep learning, big data analytics, recommender systems, intelligent optimization systems, mobile wireless networks and smart buildings.

RECENT PROJECTS (All projects)
A model for integration of urban heat island effect mitigation into planning processes: Local climate zone based morphological approach
TÜBİTAK 1001. (Researcher)
Development of software and hardware device for detection of real-time security weakness and anomaly for enterprise networks
TÜBİTAK 3191584. (Consultant)
Data collection, verification and querying from heterogeneous data sources on the Internet
TÜBİTAK BİDEB 2244 Industrial PhD Program (Huawei Technologies Co. Ltd.), 118C127. (Principal Investigator)

LATEST PUBLICATIONS (All publications)
A novel Web ranking algorithm based on pages multi-attribute
Baker M.R., Akcayol M.A.
International Journal of Information Technology, Springer (accepted).
Bird species classification using deep learning: a comparative study
Bilgin M.M., Özdem K., Akcayol M.A.
Journal of Polytechnic, (accepted).
Abstract | pdf
Studies to classify bird species on the basis of images are very difficult due to both the abundance of colors and patterns in the image, and their very close visual characteristics. In this study, six different deep learning models have been applied for the classification of bird species and the experimental results have been compared comprehensively. A dataset named 250 Bird Species, which includes a total of 31316 bird images with 225 bird species, was used as dataset. In the study, 1125 images have been used for the test and 1125 images for the validation. The comparison of VGG16, ResNet50, ResNet152V2, InceptionV3, MobileNet and DenseNet121 deep learning models have been made on the dataset respectively, according to the accuracy, precision, recall and F1-score values. In experimental studies, 94.6% accuracy value ??has been obtained with VGG16, 47.2% with ResNet50, 96.2% with ResNet152V2, 97.5% with InceptionV3, 96.9% with MobileNet and 98.2% with DenseNet121. DenseNet121 obtained the highest precision value as 0.99, sensitivity value as 0.99 and F1-score value as 0.99.
A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA
Özyurt B., Akcayol M.A.
Expert Systems with Applications, Vol.168, 114231, April 2021.
Abstract | pdf
With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.
Prediction of the next time of an event with deep learning based model
Utku A., Akcayol M.A.
Journal of Polytechnic, Vol.24(1), pp.1-15, 2021.
Abstract | pdf
Studies have been going on for many years to predict the time before some events happen. Thus, it is aimed to minimize the damage that occurs when the event occurs or to maximize the benefit to be obtained. Studies on the prediction of subsequent events in many different areas, such as the prediction of the subsequent behavior of a customer, the prediction of the subsequent occurrence of natural disasters, the estimate of the number of future demands in a given time interval, are gradually increasing. However, in the literature, there is no successful study for predicting the time and type of event before the occurrence of crimes and emergency calls. Crime analysis is a field of research aimed at securing the threatened areas, reducing the rate of crime and saving law enforcement. High success is achieved with the use of up-to-date technologies in the efforts to resolve the crime shortly after it is committed. Similarly, emergency call analysis reduces response time and optimizes resource usage. In this study, a deep learning based prediction model for crime and emergency call analysis has been developed. With the developed model, the time of the next crime and the time of the next emergency call are predicted. The results obtained with the developed model has been compared with ARIMA which is one of the statistical time series prediction methods. Experimental results have shown that the developed deep learning-based model is more successful than ARIMA in forward-looking event time prediction.
Locality sensitive hashing based clustering for large scale documents
Özdem K., Akcayol M.A.
ACM International Conference on Mathematics and Artificial Intelligence, Chengdu, China, March 19-21, 2021.
Abstract | pdf
Nowadays, the size of data continues to increase more rapidly every day. Considering this situation, large-scale processing has become a very important issue in document clustering, due to its capability to organize large numbers of documents into few meaningful and consistent clusters. In this study, a dataset consisting of 390 English textbooks of a total size of 7.61 GB, is used for the clustering task. Locality sensitive hashing and k-shingles methods are used to obtain clusters with high quality. Clusters are evaluated using cluster validity indices. According to the experimental results, high-quality clusters have been obtained, with 0.88 and 0.79 for Silhouette and Davies–Bouldin scores, respectively.
A review of image denoising with deep learning
Yapıcı A., Akcayol M.A.
IEEE International Informatics and Software Engineering Conference, Ankara, Turkey, December 16-17, 2021.
Abstract | pdf
Satellite images can be corrupted by noise during image capture, transfer or due to bad environmental conditions. In daily life and scientific searches, the need for more accurate images are increasing. However, images are distorted by noise, resulting in lower visual image quality. For this reason, noise removal studies are carried out on images to increase the quality. Until now, various methods have been proposed to decrease noise and each technique have different advantages. This paper, summarizes the studies in the field of noise reduction in video and images and compares the studies with each other.
Deep learning-based forecasting of cancellation, delay and orientation on flights
Ayaydın A., Akcayol M.A.
International Conference on Informatics and Computer Science (ICI-CS2021), Ankara, Turkey, December 9-11, 2021.
A new deep learning-based prediction model for purchase time prediction
Utku A., Akcayol M.A.
IEEE International Conference on Computer Science and Engineering, Ankara, Turkey, September 15-19, 2021.
Abstract | pdf
Nowadays, user behaviour analysis is gaining importance due to the increasing interest of users in e-commerce platforms. By providing users with a personalized shopping experience, customer satisfaction and sales rates can be increased. In this study, a deep learning based hybrid model has been developed for predicting the next purchase time. The developed model has been compared extensively with ARIMA and LSTM. Experimental results showed that the developed deep learning model has more successful than ARIMA and LSTM in predicting the next purchase time.
Bloom filter based graph database CRUD optimization for stream data
Hüseynli A., Akcayol M.A.
IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, Cracow, Poland, September 22-25, 2021.
Abstract | pdf
This study has been prepared to set light on the performance difficulties encountered in large datasets on graph databases and to increase performance in Create, Read, Update, Delete (CRUD) operations with Approximate Membership Functions (AMF). For this purpose, the Bloom filter from the AMF family is proposed in a scalable structure. Neo4j commercial graph database was preferred in experimental studies as a graph data model. In the experimental studies, it has been observed that the proposed method for all CRUD operations produces better results than the BTREE indexing method used by the database. The proposed AMF method can be preferred for performance optimization in such databases.
Long short-term memory based query auto-completion
Qureshi A.R.A., Akcayol M.A.
IEEE International Conference on Electrical and Electronics Engineering, Antalya, Turkey, April 9-11, 2021.
Abstract | pdf
In this study, Long Short-Term Memory (LSTM) based Query Auto-Completion (QAC) has been proposed to generate a query completion list using input prefix. The performance of the QAC system has been evaluated by using the relevancy score, and the quality of the QAC generation system has been evaluated by using partial and complete matching strategies, success rate, normalized discounted cumulative gain, and mean average precision. The proposed LSTM based QAC system has been extensively tested using AOL and ORCAS datasets. According to experimental results, the performance of the proposed QAC system is more successful with the partial matching strategy. Also, the quality of the QAC generation list by the proposed QAC system is better on the complete matching strategy.
Automated machine learning platform
Selvi G., Dağ G., Dirican E.G., Aktay T., Aksu S.M., Özdem K., Akcayol M.A.
IEEE International Conference on Computer Science and Engineering, Ankara, Turkey, September 15-19, 2021.
Abstract | pdf
With the rapid development of information technologies and the widespread use of the internet, the volume and diversity of data has also increased. Meaningful information and important results can be obtained by processing this data, which is expressed with the concept of big data. In this study, a machine learning platform that can automatically learn from data sets with different data types and dimensions has been developed. When the dataset of any field is given as an input to the developed automatic machine learning platform, the most appropriate machine learning model is determined. With this platform, which has a Web interface that can be easily used by people who are experts in their field but do not have sufficient knowledge in the field of machine learning and data science, the most suitable machine learning model for the data set is suggested to the users and training and test results for different models can be obtained and compared. The experimental studies have shown that the developed platform is successful in knitting a machine learning model suitable for the dataset.
Thermal infrared colorization using deep learning
Çiftçi O., Akcayol M.A.
IEEE International Conference on Electrical and Electronics Engineering, Antalya, Turkey, April 9-11, 2021.
Abstract | pdf
Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an encoder-decoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Least Absolute Deviations (L1), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM).

A comprehensive review on using of deep learning approaches in video captioning applications
Alpay Ö., Akcayol M.A.
Journal of Engineering Sciences and Design, Vol.8(5), pp.271-289, 2020.
Abstract | pdf
Video captioning is defined as creating captions for videos by automatically. It is an area of increasing interest as it includes both computer vision and natural language approaches. Producing expressions in natural language and combining them with image frames is a difficult process. Many approaches have been developed to solve this problem. In this study, a literature study about the developments in video captioning research is presented. The studies examined are examined in different categories according to the methods used. The methods are summarized and their strengths and limitations are analyzed. The methods are summarized and their strengths and limitations are analyzed. Deep learning is one of the most common methods used in this regard. Research has been conducted on the applicability of deep learning approaches in video labeling systems. Used datasets and performance evaluation criteria are analyzed. The developments in deep learning methods have provided new approaches to video captioning. The successful results have been observed with the use of deep learning methods in studies on video captioning.
Deep learning based new prediction model for the next purchase
Utku A., Akcayol M.A.
Advances in Electrical and Computer Engineering, Vol.20(2), pp.35-44, 2020.
Abstract | pdf
Time series represent the consecutive measurements taken at equally spaced time intervals. Time series prediction uses the information in a time series to predict future values. The future value prediction is important for many business and administrative decision makers especially in e-commerce. To promote business, sales prediction and sensing of future consumer behavior can help business decision makers in marketing campaigns, budget and resource planning. In this study, deep learning based a new prediction model has been developed for the time of next purchase in ecommerce. The proposed model has been extensively tested and compared with RF, ARIMA, CNN and MLP using a retail market dataset. The experimental results show that the developed model has been more successful than RF, ARIMA, CNN and MLP to predict the time of the next purchase.
Deep learning based new model for video captioning
Özer E.G., Karapınar İ.N., Başbuğ S., Turan S., Utku A., Akcayol M.A.
International Journal of Advanced Computer Science and Applications, Vol.11(3), 2020.
Abstract | pdf
Visually impaired individuals face many difficulties in their daily lives. In this study, a video captioning system has been developed for visually impaired individuals to analyze the events through real-time images and express them in meaningful sentences. It is aimed to better understand the problems experienced by visually impaired individuals in their daily lives. For this reason, the opinions and suggestions of the disabled individuals within the Altınokta Blind Association (Turkish organization of blind people) have been collected to produce more realistic solutions to their problems. In this study, MSVD which consists of 1970 YouTube clips has been used as training dataset. First, all clips have been muted so that the sounds of the clips have not been used in the sentence extraction process. The CNN and LSTM architectures have been used to create sentence and experimental results have been compared using BLEU 4, ROUGE-L and CIDEr and METEOR.
A weighted multi-attribute-based recommender system using extended user behavior analysis
Akcayol M.A., Utku A., Aydoğan E., Mutlu B.
Electronic Commerce Research and Applications, Vol.28, pp.86-93, 2018.
BibTeX | Abstract | pdf
A new weighted multi-attribute based recommender system (WMARS) has been developed using extended user behavior analysis. WMARS obtained data from number of clicked items in the recommendation list, sequence of the clicked items in recommendation the list, duration of tracking, number of tracking same item, likes/dislikes, association rules of clicked items, remarks for items. WMARS has been applied to a movie web site. The experimental results have been obtained from a total of 567 heterogeneous users, including employers in different sectors, different demographic groups, and undergraduate and graduate students. Using different weighted sets of the attributes’ parameters, WMARS has been tested and compared extensively with collaborative filtering. The experimental results show that WMARS is more successful than collaborative filtering for the data set that was used.
Calculation of electron energy distribution functions from electron swarm parameters using artificial neural network in SF6 and Argon
Tezcan S.S., Akcayol M.A., Ozerdem O.C., Dincer, M.S.
IEEE Transactions on Plasma Science, Vol.38(9), pp.2332-2339, 2010.
Abstract | pdf
This paper proposes an artificial neural network (ANN) to obtain the electron energy distribution functions (EEDFs) in SF6 and argon from the following: 1) mean energies; 2) the drift velocities; and 3) other related swarm data. In order to obtain the required swarm data, the electron swarm behavior in SF6 and argon is analyzed over the range of the density-reduced electric field strength E/N from 50 to 800 Td from a Boltzmann equation analysis based on the finite difference method under a steady-state Townsend condition. A comparison between the EEDFs calculated by the Boltzmann equation and by ANN for various values of E/N suggests that the proposed ANN yields good agreement of EEDFs with those of the Boltzmann equation solution results.
An educational tool for fuzzy logic controlled BDCM
Akcayol M.A., Çetin A., Elmas Ç.
IEEE Transactions on Education, Vol.45(1), pp.33-42, 2002.
BibTeX | Abstract | pdf
Fuzzy logic controllers (FLC) have gained popularity in the past few decades with successful implementation in many areas, including electrical machines’ drive control. Many colleges are now offering fuzzy logic courses due to successful applications of FLCs in nonlinear systems. However, teaching students a fuzzy logic controlled drive system in a laboratory, or training technical staff, is time consuming and may be an expensive task. This paper presents an educational tool for fuzzy logic controlled brushless direct current motor (BDCM), which is a part of a virtual electrical machinery laboratory project. The tool has flexible structure and graphical interface. Motor and controller parameters of the drive system can be changed easily under different operating conditions.
Application of adaptive neuro-fuzzy controller for SRM
Akcayol M.A.
Advances in Engineering Software, Vol.35(3-4), pp.129-137, 2004.
Abstract | pdf
In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed control of a switched reluctance motor (SRM). SRMs have become an attractive alternative in variable speed drives due to their advantages such as structural simplicity, high reliability, high efficiency and low cost. But, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter variations. In this study, to tackle these problems, an adaptive neurofuzzy controller is proposed. Heuristic rules are derived with the membership functions then the parameters of membership functions are tuned by ANFIS. The algorithm has been implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the proposed ANFIS controller under different operating conditions of the SRM.
NEFCLASS-based neuro fuzzy controller for SRM drive
Akcayol M.A., Elmas Ç.
Engineering Applications of Artificial Intelligence, Vol.18(5), pp.595-602, 2005.
BibTeX | Abstract | pdf
Switched reluctance motor (SRM) is increasingly employed in industrial applications where variable speed is required because of their simple construction, ease of maintenance, low cost and high efficiency. However, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter uncertainties. In this paper, to overcome this problem, a neuro fuzzy controller (NFC) is proposed. Heuristic rules are derived with the membership functions of the fuzzy variables tuned by a neural network (NN). The algorithm is implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the NFC with various working conditions of the SRM.