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1.
Kolivand Hoshang Joudaki Saba Sunar Mohd Shahrizal Tully David 《Neural computing & applications》2021,33(10):4945-4963
Neural Computing and Applications - Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people... 相似文献
2.
Izzuddin Tarmizi Ahmad Safri Norlaili Mat Othman Mohd Afzan 《Neural computing & applications》2021,33(11):6233-6246
Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with... 相似文献
3.
Mohd Hilmi Hasan Jafreezal Jaafar Mohd Fadzil Hassan 《Artificial Intelligence Review》2014,42(4):835-850
Monitoring Quality of Service (QoS) compliance is an important procedure in web service environment. It determines whether users’ expectations are met, and becomes the vital factor for them to decide whether to continue paying for the service or not. The monitoring is performed by checking the actual services performance against the QoS stated in Service Level Agreement (SLA). In relation to that, the need for monitoring vague QoS specifications in SLA has become more apparent nowadays. This paper reviews the published literature on web services QoS monitoring. A total of 60 selected articles were systematically analyzed. There were 23 of the articles selected through restrictive search criteria while the other 37 were selected based on unrestrictive search criteria. The review shows that little evidence exists on monitoring vague QoS specifications of web services. Providing ability for monitoring QoS that is specified vaguely in SLA could give new insights and implications to web services field. This paper concludes with some recommended future works to construct the theory and perform the empirical research. 相似文献
4.
5.
Hoshang Kolivand Zakiah Noh Mohd Shahrizal Sunar 《Multimedia Tools and Applications》2014,73(3):1225-1245
Implementation of shadows is crucial to enhancement of images in AR environments. Without shadows, virtual objects would look floating over the scene resulting in unrealistic rendering of AR environments. Casting hard shadows would provide only spatial information while soft shadows help improve realism of AR environments. Several algorithms have been proposed to render realistic shadows which often incurred high computational costs. Little attention has been directed towards the balanced trade-off between shadow quality and computational costs. In this study, two approaches are proposed: Quadratic Spline Interpolation (QSI) to soften the outline of the shadow and Detail Multi-Layer (DML) technique to optimize the volume of computations for the generation of soft shadows based on real light sources. QSI estimates boarder hard shadow samples while DML involves three main phases: real light sources estimation, soft shadow production and reduction of the complexity of 3-Dimensional objects’ shadows. To be more precise, a reflective hemisphere is used to capture real light and to create an environment map. The Median Cut algorithm is implemented to locate the direction of real light sources on the environment map. Subsequently, the original hard shadows are retrieved and a sample of multilayer hard shadows is produced where each layer has its unique size and colour. These layers overlap to produce soft shadows based on the real light sources’ directions. Finally, the Level of Details (LOD) algorithm is implemented to increase the efficiency of soft shadows by decreasing the complexity of vertex transformations. The proposed technique is tested using three samples of multilayer hard shadows with varying numbers of light sources generated from the Median Cut algorithm. The experimental results show that the proposed technique successfully produces realistic soft shadows at low computational costs. 相似文献
6.
Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis(ICA) features. These local features are acquired using locally lateral subspace(LLS) strategy.Then, through linear discriminant analysis(LDA) we investigate the intraclass and interclass representation of each local ICA feature and express each feature s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding(LLE) along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE,FERET and CK+ is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects. 相似文献
7.
Ismail Mohd Adnan Shahin 《International Journal of Speech Technology》2013,16(3):341-351
Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges. 相似文献
8.
Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Muhammad Faiz Misman Michifumi Yoshioka 《Artificial Life and Robotics》2009,13(2):410-413
A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used
to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer
classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes
relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification.
It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this
paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried
on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach
that used a single-objective strategy in a genetic algorithm.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
9.
Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Muhammad Faiz Misman Michifumi Yoshioka 《Artificial Life and Robotics》2009,13(2):414-417
Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes
simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient
cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the
number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small
subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset
of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods
has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
10.
Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results. 相似文献