Machine Learning and Data Mining Research Group - International Burch University
 

Machine Learning and Data Mining Research Group

About

The "machine learning" research group is a part of the Faculty of Engineering and Information Technologies at the International Burch University. It is led by Prof. Dr. Abdulhamit Subasi and counts 8 Ph.D. students representing virtually all areas of machine learning and data mining. The group focuses on machine learning and data mining research involving structured data, symbolic, logical and probabilistic representations, and background knowledge and applies it techniques to challenging domains in the life sciences and action- and activity learning.

Machine learning research at the International Burch University was initiated in 2009 when Abdulhamit Subasi comes to University and start to work as Dean of Faculty. In 2010, the group rapidly gained recognition for its seminal contributions to inductive logic programming. During this period, members of the group did a number of well-known scientific researches, many of which are now gathered in our wide-spread tool, and the activities of the group rapidly expanded into domains such as reinforcement learning and distance-based learning. Since then, the group focuses on applications in the life sciences (especially chemo- and bioinformatics), in constraint-based data mining and inductive databases, and statistical relational learning (combining probabilistic models with logic). It is now one of the largest machine learning labs groups in this part of Europe.

Research

Machine learning is the subfield of artificial intelligence and computer science that studies how machines can learn. A machine learns when it improves its performance on specific tasks with experience. In order to learn, machine learning methods analyze their past experience in order to find useful regularities, which explain why machine learning is closely related to data mining. The machine learning group is investigating all types of machine learning and data mining problems and techniques, though it focuses on dealing with structured data (such as graphs, trees, and sequences), symbolic, logical and relational representations, and the use of knowledge and constraints. The group is well-known for its work on inductive logic programming, (statistical) relational learning, relational reinforcement learning, decision tree learning, graph mining, and inductive databases and constraint-based mining. It also studies applications in the life sciences and action- and activity learning.

People
Assoc. Prof. Dr. Gunay Karli (contact person)
Assist. Prof. Dr. Nejdet Dogru
Assis. Prof. Dr. Jasmin Kevrić
Assis. Prof. Dr. Harun Šiljak
Assis. Prof. Dr. Jasna Hivziefendić
Assis. Prof. Dr. Zeynep Orhan
Assis. Prof. Dr. Amar Saric
Senior Teaching Assistant Dino Kečo, PhD Candidate
Senior Teaching Assistant Zerina Mašetić, PhD Candidate
Senior Teaching Assistant Samed Jukić, PhD Candidate
Senior Teaching Assistant Adnan Hodžić, PhD Candidate
Adnan Dželihodžić, PhD Candidate
Mirza Šarić, PhD Candidate
Lejla Bandić
Džurejdž Crnkić
Aiša Ramović
Fatima Mašić
Enis Gegić
Bećir Isaković
Medina Bandić
Publications

JOURNALS


Accepted/Published Articles in SCI indexed Journals (Peer-review)

Z. Masetic, A. Subasi; Congestive heart failure detection using random forest classifier, Computer Methods and Programs in Biomedicine, Vol. 130, Jul. 2016
Jasmin Kevric, Samed Jukic, and Abdulhamit Subasi; An effective combining classifier approach using tree algorithms for network intrusion detection, Neural Computing and Applications, Jun. 2016
M.Saric, J.Hivziefendic; Management of the Power Distribution Network Reconstruction Process Using Fuzzy Logic, Springer International Publishing, Vol. 3, Dec. 2016
Nejdet Dogru, Abdulhamit Subasi; Comparison of clustering techniques for traffic accident detection, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 23, Dec. 2015
S. Cankurt, A. Subasi, Tourism demand modelling and forecasting using data mining techniques in multivariate time series: A case study in Turkey, (Accepted Turkish Journal of Electrical Engineering & Computer Sciences)
E. Kremic, A. Subasi, Performance of Random Forest and SVM in Face Recognition (Accepted International Arab Journal of Information Technology)
M. R. Bozkurt, A. Subasi, E. Koklukaya, M. Yilmaz, “Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models”, (Accepted Turkish Journal of Electrical Engineering & Computer Sciences)
N. Dogru, A. Subasi, Comparison of clustering techniques for traffic accident detection, (Accepted Turkish Journal of Electrical Engineering & Computer Sciences)
E. Alickovic, A. Subasi, “Effect of Multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases”, Circuits Systems and Signal Processing, Feb. 2015, Vol. 34, Issue 2, 513-533.
E. Gokgoz, A. Subasi, Comparison of decision tree algorithms for EMG signal classification Biomedical Signal Processing and Control, 18 (2015), 138–144.
A. Subasi, A decision support system for Diagnosis of Neuromuscular Disorders using Evolutionary Support Vector Machines, Signal, Image and Video Processing, Vol. 9, Issue 2 (2015), Page 399-408.
H. Siljak, A. Subasi, “Fourier spectrum related properties of vibration signals in accelerated motor aging applicable for age determination”, Maintenance and Reliability 2014; 16 (4): 616–621.
H. Šiljak, A. Subasi “A novel approach to Hurst analysis of motor vibration data in aging processes”, Journal of Vibroengineering, Vol. 16, Issue 5, 2014, p. 2250‑2255.
J. Kevric, A. Subasi, “The Effect of Multiscale PCA De-noising in Epileptic Seizure Detection”, Journal of Medical Systems, 38(10):131, 1-13, 2014.
E. Gokgoz, A. Subasi, “Effect of Multiscale PCA de-noising on EMG signal classification for Diagnosis of Neuromuscular Disorders ”, Journal of Medical Systems, 38(4):31,1-10, April 2014.
A.Nuhanović, J. Hivziefendić, A. Hadžimehmedović; Distribution network reconfiguration considering power losses and outages costs using genetic algorithm, Journal of Electrical Engineering, Vol. VOL. 64, No. No 5, Jan. 2013

Accepted/Published Articles in non-SCI Journals (Peer-review)


Nuhanović, J.Hivziefendić, A.Hadžimehmedović; Distribution system planning using multi-objective NSGA II genetic optimization algorithm, 6th International Confernce on Deregulated Electricity Market Issues in South-Eastern Europe, DEMSEE, Jan. 2016
Z. Masetic, A. Subasi, J. Azemovic; Malicious Web Sites Detection using C4.5 Decision Tree, Southeast Europe Journal of Soft Computing, Vol. 5, No. 1, Mar. 2016
Samed Jukic, Jasmin Azemovic, Dino Keco, Jasmin Kevric; Comparison of Machine Learning Techniques in Spam E-mail Classification, Southeast Europe Journal of Soft Computing, Vol. 4, No. 1, Mar. 2015
S. Cankurt and A. Subasi, Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components, Balkan Journal of Electrical & Computer Engineering, 2015, Vol.3, No.1
Günay Karlı, Şenol Doğan, Adem Karadağ; Computational Approach for Promoter Identification with data Mining Techniques, International Journal of Engineering Inventions, Vol. 4, No. 1, Feb. 2014
Günay Karlı, Adem Karadağ; Genomic DNA Analysis Using ANFIS and ANN, International Journal of Engineering Research and Development, Vol. 9, No. 10, Jan. 2014
Günay Karlı, Adem Karadağ; Predicting Functional Regions in Genomic DNA Sequences Using Artificial Neural Network, International Journal of Engineering Inventions, Vol. 3, No. 6, Feb. 2014
Z. Masetic, A. Subasi, Detection of congestive heart failures using C4.5 Decision Tree, Southeast Europe Journal of Soft Computing, 2(2), 74-77, 2013.
S. Jukic, A. Subasi, Localization of the epileptogenic foci using Support Vector Machine, Southeast Europe Journal of Soft Computing, 2(2), 26-30, 2013.
E. Alickovic, A. Subasi, Usage of Simplified Fuzzy ARTMAP for improvement of classification performances, Southeast Europe Journal of Soft Computing, 2(2), 93-97, 2013.
G. Sikiric, S. Avdakovic, A. Subasi, Comparison of Machine Learning Methods for Electricity Demand Forecasting in Bosnia and Herzegovina, Southeast Europe Journal of Soft Computing, 2(2), 12-14, 2013.
D. Kečo, A. Subasi, Parallelization of genetic algorithms using Hadoop Map/Reduce, Southeast Europe Journal of Soft Computing, 1(2), 56-59, 2012.
M. A. Yaman, E. Yaman, A. Subasi, F. Rattay, “Automatic Gender Classification from Color Images Using Support Vector Machines”, International Journal of Arts & Sciences, 4(20), 279-283, 2011.
E. Yaman, M. A. Yaman, A. Subasi, F. Rattay, “EMG Signal Classification Using Decision Trees and Neural Networks”, International Journal of Arts & Sciences, 4(20), 285-292, 2011.

CONFERENCES

International conferences:

Emir Salihagic, Jasmin Kevric, Nejdet Dogru; Classification of ON-OFF states of Appliance Consumption Signatures, XI International Symposium on Telecommunications – BIHTEL 2016, Sarajevo, BiH, Oct. 2016
Adnan Hodzic, Jasmin Kevric, Ahmed Karadag; COMPARISON OF MACHINE LEARNING TECHNIQUES IN PHISHING WEBSITE CLASSIFICATION, International Conference on Economic and Social Studies (ICESoS), Sarajevo, BiH, Apr. 2016, Vol. 3, Page 249 - 256
N. Arnaut, A. Subasi, Sleep stage classification using AR Burg and C4.5 classifier, The 1st Conference of Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH 2015), 13-15 March 2015, Sarajevo, Bosnia and Herzegovina.
E. Podrug, A. Subasi, Surface EMG pattern recognition by using DWT feature extraction and SVM classifier, The 1st Conference of Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH 2015), 13-15 March 2015, Sarajevo, Bosnia and Herzegovina.
K. Hajdarevic, S. Konjicija, A. Subasi, A Low Energy APRS-IS Client-Server Infrastructure Implementation using Raspberry Pi, 22nd Telecommunications Forum (TELFOR), 2014, At Belgrade, Serbia.
K. Hajdarevic, S. Konjicija, A. Subasi, Svxlink VOIP Implementation Using Raspberry Pi in Education and Disaster Relief Situations, BIHTEL2014, Sarajevo, Bosnia and Herzegovina; 10/2014
E. Alickovic, A. Subasi, Comparison of decision tree methods for breast cancer diagnosis, The 6th International Conference on Information Technology (ICIT 2013), Amman, Jordan, May. 2013
Projects
1. Project
Project Name:
Customer Segmentation and Classification Using Data Mining Techniques

Date: Jan. 2015-
Supported by: Info Studio Company

2. Project
Project Name:
Credit Scoring for Micro Credit Companies Using Data Mining Techniques

Date: Jan. 2015-
Supported by: Info Studio Company

3. Project
Project Name:
SIFEMA Toolbox: A Set of MATLAB GUI Modules for Signal Processing and Machine Learning

Date: Oct. 2014-
Supported by: International Burch University

4. Project
Project Name:
EMG based man–machine interaction—A Platform for Classification of Myoelectric Signals for Prosthesis Control

Date: Sept. 2013-
Supported by: International Burch University

5. Project
Project Name:
Comparison of Different Feature Extraction and Machine Learning Techniques in EEG-Based Wireless BCI System

Date: Sept. 2013-
Supported by: International Burch University

6. Project
Project Name:
New Approaches for Diagnosis of Brain Diseases Using Different Image Processing and Data Mining Techniques

Date: Sept. 2013-
Supported by: International Burch University

7. Project
Project Name:
Comparison of Machine Learning Methods for Biomedical Signal Analysis

Date: Sept. 2010- May. 2012
Supported by: International Burch University

8. Project
Project Name:
Comparison of Data Mining Techniques for Robust Intrusion Detection

Date: Sept. 2010- May. 2012
Supported by: International Burch University