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Many applications require k-coverage network to ensure the quality of the monitored area. Meanwhile, preserving the required k-coverage for the maximum network lifetime with a small computation cost is a major challenge. In this paper, we propose Maximum Layers Scheduling algorithm (MLS) to preserve the k-coverage and prolong the network lifetime. MLS(More)
—Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper compares the performances of Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Controlled experimental data is used, which designed in such(More)
The conventional criterion for fracture risk assessment is measured based on bone mineral density (BMD). These are measured using bone densitometry machines. Although there is a strong association between bone strength and BMD, it cannot sufficiently predict fracture risk in osteoperotic patients. In view of this a more accurate measurement of bone strength(More)
This paper introduces the Gaussian shaped membership function to refine the Rule Based Fuzzy (RBF) image detection. It is expected that the proposed algorithm Gaussian Rule Based Fuzzy (GRBF) method can further refined the detection of periosteal and endosteal edges of hand phantom radiographs. The experimental data consists of four sets of hand phantom(More)
Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper contrasts the performances of Adaptive Network-Based Fuzzy Inference System (ANFIS), k-Nearest Neighbors (k-NN) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Preliminary data analysis is performed to analyze the(More)
This paper proposes an empirical study of the efficiency of the Seed-Based Region Growing (SBRG) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this paper, we used(More)
This study uses an empirical study of the efficiency of Particle swarm optimization (PSO) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this study, we used controlled(More)
The purpose of this paper is to study the effectiveness of a fusion of fuzzy heuristic edge detection technique into particle swarm optimization. In this paper we experiment on various fuzzy membership threshold values to understand the impact of these threshold on the final optimized edge detected. The testing data used are hand radiograph images. The main(More)
In Malaysia, although the government has promoted technology inclusion of rural area, the consideration of readiness for technology is being neglected. Research effort on technology readiness in Malaysian rural area is low. This research argues that low technology readiness may worsen the disparity of e-inclusion in Malaysia. Hence, this research is(More)