Mohd Halim Mohd Noor

Enhancing ontological reasoning with uncertainty handling for activity recognition

Ontology-based activity recognition is gaining interest due to its expressiveness and comprehensive reasoning mechanism. An obstacle to its wider use is that the imperfect observations result in failure of recognizing activities. This paper proposes a novel reasoning algorithm for activity recognition in smart environments. The algorithm integrates OWL ontological reasoning mechanism with Dempster-Shafer theory of evidence to provide support for handling uncertainty in activity recognition. It quantifies uncertainty while aggregating contextual information and provides a degree of belief that facilitates more robust decision making in activity recognition. The presented approach has been implemented and evaluated on an internal and public datasets and compared with a data-driven approach that is using hidden Markov model. Results have shown that the proposed reasoning approach can accommodate uncertainties and subsequently infer the activities more accurately in comparison with existing ontology-based recognition and perform comparably well to the data-driven approach.

Gel electrophoresis image segmentation with Otsu method based on Particle Swarm Optimization

Gel electrophoresis (GE) is an important tool in genomic analysis. It is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. Numerous pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Then multilevel thresholding using Otsu method based on Particle Swarm Optimization is applied. The experimental results show that the PSO-Otsu successfully segmented all the bands.

Intelligent Breast Cancer Diagnosis Using Hybrid GA-ANN

Breast cancer prevails as one of the infamous deathly diseases among women worldwide. Early detection and treatment of breast cancer can increase the survival rate of patients. Presently, the method of diagnosis depends on the human experiences. The method is time-consuming, subjected to human error and cause unnecessary burden to radiologists. This paper introduces an automatic breast cancer diagnosis technique using a genetic algorithm (GA) for simultaneous feature selection and parameter optimization of artificial neural networks (ANN). The performances of the proposed algorithm employing three different variations of the backpropagation technique for the fine tuning of the weight of ANN are compared. The algorithm is called the GAANN_XX where the XX refers to the back-propagation training variation used. The proposed algorithms called GAANN_RP produces the best and average, 99.43% and 98.29% correct classification respectively on the Wiscinson Breast Cancer Dataset.

Multilevel thresholding of gel electrophoresis images using firefly algorithm

Gel electrophoresis (GE) is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. A few pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Multilevel thresholding using Otsu method based on Firefly Algorithm is developed. The experimental results show that the Otsu-FA produced good separation of DNA bands and its background.

Compact single hidden layer feedforward network for mycobacterium tuberculosis detection

Advances in imaging technology and artificial intelligence have greatly enhanced the research and development of computer-aided tuberculosis (TB) diagnosis system. The system aims to assist medical technologist and improve the accuracy of clinical diagnosis. A typical architecture of a computer-aided TB diagnosis system consists of image processing, feature extraction and classification. Finding an effective classifier for the system has been regarded as a critical topic, in order to improve the detection performance and avoid making false decision. In this study, the recent method called compact single hidden layer feedforward neural network (C-SLFN) trained by an improved Extreme Learning Machine (ELM) is evaluated for detecting the TB bacilli. Six affine moment invariants are extracted from segmented tissue slide images and fed into the C-SLFN. The network is trained and classified the input patterns into three classes: ‘TB’, ‘overlapped TB’ and ‘non-TB’. Further, the study compares the network performance with a SLFN trained using the standard ELM algorithm. The results obtained from this study suggested that the standard ELM still outperformed the C-SLFN in term of classification accuracy. The standard ELM, however requires a large number of hidden nodes compares to the C-SLFN.

Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer

Abstract Previous studies on physical activity recognition have utilized various fixed window sizes for signal segmentation targeting specific activities. Naturally, an optimum window size varies depending on the characteristics of activity signals and fixed window size will not produce good segmentation for all activities. This paper presents a novel approach to activity signal segmentation for physical activity recognition. Central to the approach is that the window size is adaptively adjusted according to the probability of the signal belongs to a particular activity to achieve the most effective segmentation. In addition, an activity transition diagram for activity recognition is developed to validate the activity transition and improve recognition accuracy. The adaptive sliding window segmentation algorithm and the role of activity transition diagram are described in the context of physical activity recognition. The approach recognizes not only well defined static and dynamic activities, but also transitional activities. The presented approach has been implemented, evaluated and compared with an existing state-of-the-art approach by using internal and public datasets which contains activity signals of dynamic, static and transitional activities. Results have shown that the proposed adaptive sliding window segmentation achieves overall accuracy of 95.4% in all activities considered in the experiments compared to the existing approach which achieved an overall accuracy of 89.9%. The proposed approach achieved an overall accuracy of 96.5% compared to 91.9% overall accuracy with the existing approach when tested on the public dataset.

Gel Electrophoresis Image Segmentation with Kapur Method Based on Particle Swarm Optimization

Gel electrophoresis (GE) is an important tool in genomic analysis. It is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. Numerous pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Then multilevel thresholding using Otsu method based on Particle Swarm Optimization is applied. The experimental results show that the PSO-Otsu successfully segmented all the bands.

Moving vehicle segmentation in a dynamic background using self-adaptive kalman background method

This paper introduce the adaptive kalman filter to modeling dynamic background for background subtraction. Background subtraction is a method to identify object and famous used in moving object segmentation. In this paper we also investigate a comparison study on Gaussian subtraction method, frame differencing method and approximate median method. The detection of object will be shown in the result.

Self adaptive neuro-fuzzy control of FES-assisted paraplegics indoor rowing exercise

This paper describes the development of a self adaptive neuro-fuzzy control mechanism for FES-assisted indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of function of lower body through the application of functional electrical stimulation (FES). The neuro-fuzzy control technique is a control technique that combines fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. An adaptive control strategy is purposed to control the FES-rowing with the adaptation of the muscle fitness of the physiological based muscle model in the FLC. The adaptive control is able to modify the control law used by the FLC to cope with the muscle fatigue in adjusting the rowing ergometer inclination and generating the stimulation pulse width required by the system. This study indicates that the self adaptive neuro-fuzzy control developed provides an effective mechanism for automatically adjusting the ergometer inclination and regulating the stimulation pulse width for FES-rowing to overcome muscle fatigue.

Evaluation of spreading factor inertial weight PSO for FLC of FES-assisted paraplegic indoor rowing exercise

This paper describes the evaluation of the spreading factor inertia weight Particle Swarm Optimization (PSO) for the fuzzy logic control (FLC) of FES-assisted paraplegic indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of lower extremities through the application of functional electrical stimulation (FES). FLC is used to control the knee trajectories for smooth rowing manoeuvre and minimize the total electrical stimulation required by the muscles. PSO is implemented to optimize the parameter of the FLC. The objective function specified is to minimize the mean squared error of knee angle trajectory. The inertia weight of the PSO is updated using spreading factor technique and it performance is compared to the performance of PSO with time variant inertia weight. In view of good results obtained, it is concluded that Spreading Factor Inertia weight PSO is able to obtain the optimal parameter for FLC of FES-rowing.