Suwanna Rasmequan

A new paradigm for computer-based decision support

We identify and address a fundamental general problem which we regard as crucial for the widespread, effective use of decision support systems (DSS) in the future: how can we substantially improve the quality of interaction, and the degree of flexible engagement, between humans and computers? Rather than seeking an answer in additional technical functionality, we propose a new paradigm for computing that is human-centered and that adopts a novel, observation-oriented approach to data modelling. We report a recent practical work (a timetabling instrument) showing an unusual degree of openness for interaction, and we give evidence that our approach can encompass conventional tools such as expert systems.

Migration planning using modified Cuckoo Search Algorithm

Mobile agent is a distributed computing technology which allows the agents to carry their jobs on behalf of users from current processing server to the others. Hence, the migration planning is a significant issue for improving performance of mobile agent. In this paper, we propose an improving method for finding path of mobile agent migration planning based on Cuckoo Search Algorithm. Our proposed method modified the original Cuckoo Search Algorithm in two ways. That is, for the first one: in finding new nest, we propose random replacement instead of Lévy fight algorithm. Then, for the second one: in destroying egg, we propose a context sensitive parameter instead of a constant parameter. The experimental results, comparing with migration using the former proposed method, original Cuckoo Search Algorithm and Ant Colony System, confirm that the proposed migration algorithm improves the path finding within the limited time scale.

Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms

A new aspect of imbalanced data classification was studied. Unlike the classical imbalanced data classification where the cause of problem is due to the difference of data sizes, our study concerns only the situation when there exists an overlap between two classes. When one class overlaps another class, there are three regions induced from the overlap. The first region is the overlapped region between two classes. The rest is the non-overlapped region of each class. The imbalance situation is obviously caused by the different amount of data at the overlapped region and non-overlapped region. In this situation, the difference of data sizes from different classes is not the main concern and has no effect on the accuracy of classification. In this research, a combined technique, called Soft-Hybrid algorithm, was proposed for improving classification performance. The technique was divided into two main phases: boundary region determination and responsive classification algorithms for each sub-area. In the first phase, data were grouped as (1) non-overlapping data, (2) borderline data, and (3) overlapping data. Learning data using modified Hausdorff Distance, Radial Basis Function Network and K-Means clustering technique with Mahalanobis Distance. Then, modified Kernel Learning Method, modified DBSCAN and RBF network were applied to classify the data into proper groups based on statistical values from the classification phase. Finally, the results of all techniques were combined. The experimental results illustrated that the proposed method can significantly improve the effectiveness in classifying imbalanced data having large overlapping sections based on TP rate, F-measure and G-mean measures. Moreover, the computational times of the proposed method were lower than the standard algorithms used for this type of this problem.

Fall detection using directional bounding box

Falls are significant public health problem. In the last few years, several researches based on computer vision system have been developed to detect a person who has fallen to the ground. This paper presents a novel fall detection technique namely the directional bounding box (DBB) to detect a falls event especially a situation of fall direction paralleling the line of cameras sight. The DBB is constructed with perspective side view transformation of depth information. Moreover, a new aspect ratio namely the center of gravity point (COG) is proposed to monitor human movement. The proposed technique was evaluated with the video data set gathering from a RGB-D sensor. The experimental result of the proposed technique was better both accuracy and response times than previous works.

Automatic exudates detection in retinal images using efficient integrated approaches

Diabetic Retinopathy with exudates causes a major problem in human visualization and becomes a cause of blindness to diabetic patients. In addition, the numbers of diabetic retinopathy patients are increasing while the numbers of doctors are not easily increased in the same proportion. This circumstance causes a heavy work load for doctors. In the past, the medical image processing research has shown that simply getting a second opinion can significantly help physicians diagnosis. This research proposes a method to detect exudates from diabetic retinopathy images. The early exudates detection of diabetic retinopathy patients will reduce seriousness in diabetic retinopathy. The proposed method for detecting exudates consists of 5 major steps as follows: 1) To improve the quality of images by using the contrast limited adaptive histogram equalization (CLAHE) 2) To apply the object attribute thresholding algorithm (OAT) for non-retinal object removal, 3) To implement Frangis algorithm based on Hessian filtering for blood vessel detection 4) To detect the retinal optic disc by applying the combination between multi-resolution analysis and Hough transform and 5) To classify exudates in the remaining region with algorithms of hierarchical fuzzy-c-mean clustering. The performance of the proposed method is evaluated on DIARETDB, which is the retinal image database of the Lappeenranta University of Technology, where the performance is good enough for exudates detection.

Cognitive artefacts for decision support

We introduce a novel approach to computer-based modelling which allows the construction of cognitive artefacts with an unusual degree of openness and a potential for close integration with other artefacts and with human processes. Four particular artefacts concerning railway operation, timetabling, restaurant management and warehouse management are described briefly and attention is drawn to those properties concerning knowledge representation and communication which show that the modelling methods used have a significant contribution to make the development of more effective decision support systems in a business context.

Semi-automated detection of breast mass spiculation using active contour

The use of computer research for breast cancer diagnosis in digital mammograms has been studied by some researchers for years. The researches based on medical image processing were developed and published continuously. Theirs objective are to create a diagnostic tool that can increase the accuracy of risk analysis for breast cancer. At the early stage, cancers may be identified as spiculated masses revealing architectural distortion. This research proposes a semi-automated method to detect architectural distortion characterized by thin lines radiating from its margins. It will help physicians as second or minor opinion before biopsy operation. The proposed method involves following major steps in sequence. A combination of the object attributes thresholding, hill-climbing and region growing algorithm is applied to digital mammogram for background and breast pectoral muscle removal. The second is a region of interest (ROI) selection based on image splitting and breast ratio estimation. In the third step, the shade corrections of ROI are considered by using the contrast-limited adaptive histogram equalization. Next, we apply the modified hierarchical clustering to detect and enhance the possible cluster of spiculated masses. The other clusters will be a significant reduction. The final step is established to segment spiculated shape by employing the parametric active contour method. The numerical experiments of the proposed method are performed by testing on the digital database for screening mammography (DDSM) made up by the University of South Florida.

Intrusion feature selection using Modified Heuristic Greedy Algorithm of Itemset

This paper proposes the Modified Heuristic Greedy Algorithm of Itemset (MHGIS) as a feature selection method for Network Intrusion Data. The proposed method can be use as an alternative method to gain the proper attributes for the proposed domain data: Network Intrusion Data. MHGIS is modified from original Heuristic Greedy Algorithm of Itemset (HGIS) to increase efficiency for finding proper feature. In our work, we compare our result with the common method of feature selection is which the Chi-Square (Chi2) feature selection. There are 4 main steps in our experiment: Firstly, we start with data pre-processing to discard unnecessary attributes. Secondly, MHGIS feature selection and Chi2 feature selection have been employed on the pre-processed data, to reduce the number of attributes. Thirdly, we measure the recognition performance by using supervised learning algorithms which are C4.5, BPNN, RBF and SVM. Lastly, we evaluate the results received from MHGIS and Chi2. From the KDDCup99 dataset, we got 13,499 randomly sampling patterns with 34 data dimensions. With the use of MHGIS and Chi2 algorithms, we obtain 14 and 26 features respectively. The result shows that, the classification accuracies measure by C4.5 over the MHGIS selection algorithm produces better accuracies as compare to the Chi2 feature selection and HGIS feature selection over all types of classification methods.

Vertebral pose estimation using horizontal gradient vector field

Automatic and accurate detection of vertebral pose of low resolution x-ray images with different bone structure is essential to diagnose patient condition. Low resolution and incomplete x-ray image is created from low level of irradiating that can avoid radiation over doze. In this research, we propose a novel approach to estimate vertebral pose. There are three main steps including Vertical Projection, Gradient Vector Flow and Feature of Vector Field. Firstly, we segment vertebral zone from full image using Vertical Projection Average with Normal Distribution. Secondly, we introduce Gradient Vector Flow to identify background and foreground of image. Energy calculation helps to divide different objects on image. Then vector field algorithm will help to promote the exact border of the object. Finally, the horizontal field is chosen as the best information to identify the vertebral pose. From the experimental results, we can extract six main bone layouts. There are two normal layouts and four abnormal layouts. For each layout, we deploy different criteria to identify vertebral pose. For the layouts that located between 0-67 index, the separation hyperplane needs to be shifted up, otherwise shifted down. We use 80 low resolution and incomplete x-ray images from a local hospital. The accuracy rate is 79.25% compared with the ground-truth.

Vertebral pose segmentation on low radiation image using Convergence Gravity Force

Vertebral pose segmentation is an important factor in diagnosing diseases such as osteoporosis, osteopenia and scoliosis. Low radiation X-ray images are often used to diagnose such diseases. This has been done to reduce patients risk exposure of over dose radiation which may cause from a series of treatments. In this respect, it led to a low accuracy in vertebral pose detection. In this paper, we proposed to improve the automate segmentation of low quality image of vertebral pose with a more generalized technique. In the proposed method, there are three main steps. Firstly, in the pre-processing step, Auto Cropped, Multi-Threshold and Canny Edge Detection are applied to find the vertebral bone structure from the original image. Secondly, Feature Analysis and Gravity Force were used to find the region of interest or the area of each pose. Finally, Colormaps, Intensity Diagnosis and Angle Analysis are adopted to segment each vertebral pose from candidate areas retrieved from second step. The experimental results which were compared with ground truth shown that the proposed approach can estimate vertebral pose with Precision at 79.61% and Recall at 77.11%.