Mohd Hanafi Ahmad Hijazi

Self-adaptive population sizing for a tune-free differential evolution

The study and research of evolutionary algorithms (EAs) is getting great attention in recent years. Although EAs have earned extensive acceptance through numerous successful applications in many fields, the problem of finding the best combination of evolutionary parameters especially for population size that need the manual settings by the user is still unresolved. In this paper, our system is focusing on differential evolution (DE) and its control parameters. To overcome the problem, two new systems were carried out for the self-adaptive population size to test two different methodologies (absolute encoding and relative encoding) in DE and compared their performances against the original DE. Fifty runs are conducted for every 20 well-known benchmark problems to test on every proposed algorithm in this paper to achieve the function optimization without explicit parameter tuning in DE. The empirical testing results showed that DE with self-adaptive population size using relative encoding performed well in terms of the average performance as well as stability compared to absolute encoding version as well as the original DE.

Data mining techniques for the screening of age-related macular degeneration

Age related macular degeneration (AMD) is the primary cause of adult blindness. Currently AMD cannot be cured, however early detection does allow the progress of the condition to be inhibited. One of the first symptoms of AMD is the presence of fatty deposits, called drusen, on the retina. The presence of drusen may be identified through the manual inspection/screening of retinal images. This task, however, requires recourse to domain experts and is therefore resource intensive. This paper proposes and compares two data mining techniques to support the automated screening for AMD. The first uses spatial-histograms, that maintain both image colour and spatial information, for the image representation; to which a case based reasoning (CBR) classification technique is applied. The second is founded on a hierarchical decomposition of the image set so that a tree representation is generated. A weighted frequent sub-graph mining technique is then applied to this representation to identify sub-trees that frequently occur across the data set. The identified sub-trees are then encoded in the form of feature vectors to which standard classification techniques can be applied.

Retinal image classification using a histogram based approach

An approach to classifying retinal images using a histogram based representation is described. More specifically, a two stage Case Based Reasoning (CBR) approach is proposed, to be applied to histogram represented retina images to identify Age-related Macular Degeneration (AMD). To measure the similarity between histograms, a time series analysis technique, Dynamic Time Warping (DTW), is employed. The advocated approach utilises two “case bases” for the classification process. The first case base consists of green and saturation histograms with retinal blood vessels removed. The second case base comprises the same histograms, but with the Optic Disc (OD) removed as well. The reported experiments demonstrate that the proposed two stage classification process outperforms the single stage classification process with respect to a number of evaluation metrics: specificity, sensitivity and accuracy.

Fast lane detection with Randomized Hough Transform

Lane detection is an essential component of autonomous mobile robot applications. Any lane detection method has to deal with the varying conditions of the lane and surrounding that the robot would encounter while moving. Lane detection procedure can provide estimates for the position and orientation of the robot within the lane and also can provide a reference system for locating other obstacles in the path of the robot. In this paper we present a method for lane detection in video frames of a camera mounted on top of the mobile robot. Given video input from the camera, the gradient of the current lane in the near field of view are automatically detected. Randomized Hough Transform is used for extracting parametric curves from the images acquired. A priori knowledge of the lane position is assumed for better accuracy of lane detection.

Retinal Image Classification for the Screening of Age-Related Macular Degeneration

Age-related Macular Degeneration (AMD) is the most common cause of blindness in old-age. Early identification of AMD can allow for mitigation (but not cure). One of the fist symptoms of AMD is the presence of fatty deposits, called drusen, on the retina. The presence of drusen may be identified through inspection of retina images. Given the aging global population, the prevalence of AMD is increasing. Many health authorities therefore run screening programmes. The automation, or at least partial automation, of retina image screening is therefore seen as beneficial. This paper describes a Case Based Reasoning (CBR) approach to retina image classification to provide support for AMD screening programmes. In the proposed approach images are represented in the form of spatial-histograms that store both colour and spatial image information. Each retina image is represented using a series of histograms each encapsulated as a time series curve. The Case Base (CB) is populated with a labelled set of such curves. New cases are classified by finding the most similar case (curve) in the CB. Similarity checking is achieved using the Dynamic Time warping (DTW).

Image classification for age-related macular degeneration screening using hierarchical image decompositions and graph mining

Age-related Macular Degeneration (AMD) is the most common cause of adult blindness in the developed world. This paper describes a new image mining technique to perform automated detection of AMD from colour fundus photographs. The technique comprises a novel hierarchical image decomposition mechanism founded on a circular and angular partitioning. The resulting decomposition is then stored in a tree structure to which a weighted frequent sub-tree mining algorithm is applied. The identified sub-graphs are then incorporated into a feature vector representation (one vector per image) to which classification techniques can be applied. The results show that the proposed approach performs both efficiently and accurately.

Image classification using histograms and time series analysis: a study of age-related macular degeneration screening in retinal image data

An approach to image mining is described that combines a histogram based representation with a time series analysis technique. More specifically a Dynamic Time Warping (DTW) approach is applied to histogram represented image sets that have been enhanced using CLAHE and noise removal. The focus of the work is the screening (classification) of retinal image sets to identify age-related macular degeneration (AMD). Results are reported from experiments conducted to compare different image enhancement techniques, combination of two different histograms for image classification, and different histogram based approaches. The experiments demonstrated that: the image enhancement techniques produce improved results, the usage of two histograms improved the classifier performance, and that the proposed DTW procedure out-performs other histogram based techniques in terms of classification accuracy.

Web Page Recommendation Model for Web Personalization

Web usage mining has gained more popularity among researchers in discovering the users browsing behavior mining the web server log that records all the users’ transactions activities. In this paper, we developed a usage model for predictions based on association rule. Similarity between items contained in the active user profile will be calculated upon the matched rules and finally the top-N most similar items are then recommended to the user. We used the time spent on each page for weighting the pages instead of binary. Two evaluation metrics were applied to evaluate the accuracy of the recommendations, namely precision and coverage.

Empirical testing on 3-Parents Differential Evolution (3PDE) for unconstrained function optimization

The objective of this paper is to investigate whether the performance of the self-adaptive the parameters in 3PDE can improve the performance for function optimization. In this paper, we firstly propose three new algorithms (3PDE-SACr, 3PDE-SAF and 3PDE-SACrF). The preliminary testing is carried out to compare their performance with 3PDE to determine the best algorithm for the next step to self-adapt the population size. Here, the best algorithm from the preliminary testing will be chosen for the testing on self-adapting the population size in absolute and relative encodings. The preliminary testing showed that 3PDE-SAF performed the best for the first three proposed algorithms. So, 3PDE-SAF is chosen for the self-adaptive population size to test in absolute (3PDE-SAF-Abs) and relative (3PDE-SAF-Rel) encodings and the final result showed that 3PDE-SAF-Rel performed slightly better than all the proposed algorithms in terms of its average performance and its stability.

A new model of application response time for VoIP over WLAN and fixed WiMAX

The Voice over Internet Protocol (VoIP) and Wireless Local Area Networks (WLANs) has observed the fastest growth in the world of communication. The WLAN is the most assuring of technologies among the wireless networks, which has facilitated high-rate voice services at low cost and good flexibility over IP-based networks. Worldwide Interoperability for Microwave Access (WiMAX) technology is also a preliminary step to develop Fourth Generation networks (4G) technologies. WiMAX is a recent wireless broadband standard that has promised high bandwidth over long-range transmission. The standard specifies the air interface, including the Medium Access Control (MAC) and Physical (PHY) layers, of Broadband Wireless Access (BWA). It has not only succeeded in the utilization of several of the latest telecommunication techniques in the form of unique practical standards, but also paved the way for the quantitative and qualitative developments of high-speed broadband access. The Institute of Electrical and Electronics Engineers (IEEE 802.16) Standard introduces several advantages; one of them is support for Quality of Services (QoS) at the Media Access Control (MAC) level. However, the existing VoIP over WLAN and WiMAX architecture does not provide sufficient QoS in both networks. In this paper we provide an overview and develops a new distributed model to improve QoS performance for VoIP over WLAN and Fixed WiMAX network with respect to Application Response Time (ART). The model was simulated in the OPNET modeler (16.0) with Multiple Access Points (APs), Base Stations (BSs)as well as mobile devices, Subscribers Stations (SSs), and some server BSs that were selected based on Nearest Neighborhood Algorithm and Orthogonal Frequency Division Multiplexing (OFDM) techniques. The results obtained from this proposed model showed significant performance in network application response time.