Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.</p

Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%

Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%

Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%