Khashman Adnan

Work place: Founder (and Director), Center of Innovation for Artificial Intelligence British University of Nicosia, Via Mersin 10, Turkey



Research Interests: Computer systems and computational processes, Pattern Recognition, Computer Architecture and Organization, Image Processing, Data Structures and Algorithms


Khashman Adnan received the B.Eng. degree in electronic and communication engineering from University of Birmingham, England, UK, in 1991, and the M.S and Ph.D. degrees in electronic engineering from University of Nottingham, England, UK, in 1992 and 1997.

During 1998-2001 he was an Assistant Professor and the Chairman of Computer Engineering Department, Near East University, Lefkosa, Turkey. During 2001-2009 he was an Associate Professor and Chairman of Electrical and Electronic Engineering Department, and in 2009 he received the professorship. In 2001 he established the Intelligent Systems Research Group (ISRG) at the same university, and has been chairing ISRG until present. From 2007 until 2008 he was also the Vice-Dean of Engineering Faculty, later on from 2013 until 2014 the Dean of the faculty at the same university.

Since 2014, Professor Khashman was appointed as the Founding Dean of Engineering Faculty at the British University of Nicosia in N. Cyprus, where he also established the Centre of Innovation for Artificial Intelligence His current research interests include image processing, pattern recognition, emotional modeling and intelligent systems.


Author Articles
Heart Diseases Diagnosis Using Neural Networks Arbitration

By Ebenezer Obaloluwa Olaniyi Oyebade Kayode Oyedotun Khashman Adnan

DOI:, Pub. Date: 8 Nov. 2015

There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.

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Data Mining of Students’ Performance: Turkish Students as a Case Study

By Oyebade Kayode Oyedotun Sam Nii Tackie Ebenezer Obaloluwa Olaniyi Khashman Adnan

DOI:, Pub. Date: 8 Aug. 2015

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task. The performances obtained from these networks were evaluated in consideration of achieved recognition rates and training time.

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Deep Learning in Character Recognition Considering Pattern Invariance Constraints

By Oyebade Kayode Oyedotun Ebenezer Obaloluwa Olaniyi Khashman Adnan

DOI:, Pub. Date: 8 Jun. 2015

Character recognition is a field of machine learning that has been under research for several decades. The particular success of neural networks in pattern recognition and therefore character recognition is laudable. Research has also long shown that a single hidden layer network has the capability to approximate any function; while, the problems associated with training deep networks therefore led to little attention given to it. Recently, the breakthrough in training deep networks through various pre-training schemes have led to the resurgence and massive interest in them, significantly outperforming shallow networks in several pattern recognition contests; moreover the more elaborate distributed representation of knowledge present in the different hidden layers concords with findings on the biological visual cortex. This research work reviews some of the most successful pre-training approaches to initializing deep networks such as stacked auto encoders, and deep belief networks based on achieved error rates. More importantly, this research also parallels investigating the performance of deep networks on some common problems associated with pattern recognition systems such as translational invariance, rotational invariance, scale mismatch, and noise. To achieve this, Yoruba vowel characters databases have been used in this research.

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