Showing papers by "Adnan Khashman published in 2015"

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

Abstract: 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.

Deep Learning in Character Recognition Considering Pattern Invariance Constraints

Abstract: 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.

Hybrid auto encoder network for iris nevus diagnosis considering potential malignancy

Abstract: Iris nevus can be described as a growth commonly found on the iris, or regions surrounding the pupil. This growth is usually pigmented and non-cancerous, and therefore harmless; often requiring little medical attention. However, it has been established that there exists a relatively high risk of transformation of such growths into iris melanoma, which is cancerous or malignant. Furthermore, it has been shown that iris nevus infected patients also run risk of developing secondary glaucoma which requires very crucial medical intervention. Considering the above mentioned severe medical conditions that are associated with iris nevus, its diagnosis hence becomes very important. Generally, the diagnosis of iris nevus is achieved by examining eye images of patients taken by a medical expert. However, diagnosis is not an easily achievable task considering how racial and environmental factors affect the colour of patients' irises and pupils; hence pigmented growths may be concealed from a medical examiner. Also, factors such as stress and fatigue from examiners can lead to erroneous diagnosis. This research presents the use of trained hybrid auto encoders in the intelligent diagnosis of iris nevus. It is suggested that the use of the designed system as described in this work can significantly raise the confidence of medical diagnosis.