H.3. Artificial Intelligence
Amirhossein Khabbaz; Mansoor Fateh; Ali Pouyan; Mohsen Rezvani
Abstract
Autism spectrum disorder (ASD) is a collection of inconstant characteristics. Anomalies in reciprocal social communications and disabilities in perceiving communication patterns characterize These features. Also, exclusive repeated interests and actions identify ASD. Computer games have affirmative effects ...
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Autism spectrum disorder (ASD) is a collection of inconstant characteristics. Anomalies in reciprocal social communications and disabilities in perceiving communication patterns characterize These features. Also, exclusive repeated interests and actions identify ASD. Computer games have affirmative effects on autistic children. Serious games have been widely used to elevate the ability to communicate with other individuals in these children. In this paper, we propose an adaptive serious game to rate the social skills of autistic children. The proposed serious game employs a reinforcement learning mechanism to learn such ratings adaptively for the players. It uses fuzzy logic to estimate the communication skills of autistic children. The game adapts itself to the level of the child with autism. For that matter, it uses an intelligent agent to tune the challenges through playtime. To dynamically evaluate the communication skills of these children, the game challenges may grow harder based on the development of a child's skills through playtime. We also employ fuzzy logic to estimate the playing abilities of the player periodically. Fifteen autistic children participated in experiments to evaluate the presented serious game. The experimental results show that the proposed method is effective in the communication skill of autistic children.
H.3.2.6. Games and infotainment
A.H. Khabbaz; A. Pouyan; M. Fateh; V. Abolghasemi
Abstract
This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on ...
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This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on fuzzy logic to evaluate the social ability level of the children with ASD. The game adapts itself to the level of the autistic patient by reducing or increasing the challenges in the game via an intelligent agent during the play time. This task is accomplished by making more elements and reshaping them to a variety of real world shapes and redesigning their motions and speed. If autistic patient's communication level grows during the playtime, the challenges of game may become harder to make a dynamic procedure for evaluation. At each step or state, using fuzzy logic, the level of the player is estimated based on some attributes such as average of the distances between the fixed points gazed by the player, or number of the correct answers selected by the player divided by the number of the questioned objects. This paper offers the usage of dynamic AI difficulty system proposing a concept to enhance the conversation skills in autistic children. The proposed game is tested by participating of 3 autistic children. Each of them played the game in 5 turns. The results displays that the method is useful in the long-term.
H.3. Artificial Intelligence
V. Ghasemi; A. Pouyan; M. Sharifi
Abstract
This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using ...
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This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using beta probability distribution function. The frames of opinion owners are derived automatically for activities, to achieve more flexibility and extensibility. Our method is verified via two experiments. In the first experiment, it is compared to a naïve Bayes approach and three ontology based methods. In this experiment our method outperforms the naïve Bayes classifier, having 88.9% accuracy. However, it is comparable and similar to the ontology based schemes, but since no manual ontology definition is needed, our method is more flexible and extensible than the previous ones. In the second experiment, a larger dataset is used and our method is compared to three approaches which are based on naïve Bayes classifiers, hidden Markov models, and hidden semi Markov models. Three features are extracted from sensors’ data and incorporated in the benchmark methods, making nine implementations. In this experiment our method shows an accuracy of 94.2% that in most of the cases outperforms the benchmark methods, or is comparable to them.
H.3.12. Distributed Artificial Intelligence
Z. Amiri; A. Pouyan; H Mashayekhi
Abstract
Recently, data collection from seabed by means of underwater wireless sensor networks (UWSN) has attracted considerable attention. Autonomous underwater vehicles (AUVs) are increasingly used as UWSNs in underwater missions. Events and environmental parameters in underwater regions have a stochastic nature. ...
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Recently, data collection from seabed by means of underwater wireless sensor networks (UWSN) has attracted considerable attention. Autonomous underwater vehicles (AUVs) are increasingly used as UWSNs in underwater missions. Events and environmental parameters in underwater regions have a stochastic nature. The target area must be covered by sensors to observe and report events. A ‘topology control algorithm’ characterizes how well a sensing field is monitored and how well pairs of sensors are mutually connected in UWSNs. It is prohibitive to use a central controller to guide AUVs’ behavior due to ever changing, unknown environmental conditions, limited bandwidth and lossy communication media. In this research, a completely decentralized three-dimensional topology control algorithm for AUVs is proposed. It is aimed at achieving maximal coverage of the target area. The algorithm enables AUVs to autonomously decide on and adjust their speed and direction based on the information collected from their neighbors. Each AUV selects the best movement at each step by independently executing a Particle Swarm Optimization (PSO) algorithm. In the fitness function, the global average neighborhood degree is used as the upper limit of the number of neighbors of each AUV. Experimental results show that limiting number of neighbors for each AUV can lead to more uniform network topologies with larger coverage. It is further shown that the proposed algorithm is more efficient in terms of major network parameters such as target area coverage, deployment time, and average travelled distance by the AUVs.
Hossein Shahamat; Ali A. Pouyan
Abstract
In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component ...
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In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component analysis (ICA) is used for further data analysis. It estimates independent components (ICs) of PCA results. For feature extraction, local binary patterns (LBP) technique is applied on the ICs. It transforms the ICs into spatial histograms of LBP values. For feature selection, genetic algorithm (GA) is used to obtain a set of features with large discrimination power. In the next step of feature selection, linear discriminant analysis (LDA) is applied to further extract features that maximize the ratio of between-class and within-class variability. Finally, a test subject is classified into schizophrenia or control group using a Euclidean distance based classifier and a majority vote method. In this paper, a leave-one-out cross validation method is used for performance evaluation. Experimental results prove that the proposed method has an acceptable accuracy.
Seyed M. Hosseinirad; M. Niazi; J Pourdeilami; S. K. Basu; A. A. Pouyan
Abstract
In Wireless Sensor Networks (WSNs), localization algorithms could be range-based or range-free. The Approximate Point in Triangle (APIT) is a range-free approach. We propose modification of the APIT algorithm and refer as modified-APIT. We select suitable triangles with appropriate distance between anchors ...
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In Wireless Sensor Networks (WSNs), localization algorithms could be range-based or range-free. The Approximate Point in Triangle (APIT) is a range-free approach. We propose modification of the APIT algorithm and refer as modified-APIT. We select suitable triangles with appropriate distance between anchors to reduce PIT test errors (edge effect and non-uniform placement of neighbours) in APIT algorithm. To reduce the computational load and avoid useless anchors selection, we propose to segment the application area to four non-overlapping and four overlapping sub-regions. Our results show that the modified-APIT algorithm has better performance in terms of average error and time requirement for all sizes of network with random and grid deployments. For increasing the accuracy of localization and reduction of computation time, every sub-region should contain minimum 5 anchors. The modified-APIT has better performance for different sizes of network for both grid and random deployments in terms of average error and time requirement. Variations of the size of a network and radio communication radius of anchors affect the value of average error and time requirement. To have more accurate location estimation, 5 to 10 anchors per sub-region are effective in modified-APIT.
S. Arastehfar; Ali A. Pouyan; A. Jalalian
Abstract
In this paper, a novel decision based median (DBM) filter for enhancing MR images has been proposed. The method is based on eliminating impulse noise from MR images. A median-based method to remove impulse noise from digital MR images has been developed. Each pixel is leveled from black to white like ...
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In this paper, a novel decision based median (DBM) filter for enhancing MR images has been proposed. The method is based on eliminating impulse noise from MR images. A median-based method to remove impulse noise from digital MR images has been developed. Each pixel is leveled from black to white like gray-level. The method is adjusted in order to decide whether the median operation can be applied on a pixel. The main deficiency in conventional median filter approaches is that all pixels are filtered with no concern about healthy pixels. In this research, to suppress this deficiency, noisy pixels are initially detected, and then the filtering operation is applied on them. The proposed decision method (DM) is simple and leads to fast filtering. The results are more accurate than other conventional filters. Moreover, DM adjusts itself based on the conditions of local detections. In other words, DM operation on detecting a pixel as a noise depends on the previous decision. As a considerable advantage, some unnecessary median operations are eliminated and the number of median operations reduces drastically by using DM. Decision method leads to more acceptable results in scenarios with high noise density. Furthermore, the proposed method reduces the probability of detecting noise-free pixels as noisy pixels and vice versa.
