Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, ...numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
In this paper, we present a first attempt at self-adapting the population size parameter in addition to self-adapting crossover and mutation rates for the Differential Evolution (DE) algorithm. The ...objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using De Jong’s F1-F5 benchmark test problems, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm performed better in terms of best solution found than the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.
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FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge ...captured is not transparent and cannot be verified by domain experts. In this paper, Artificial Neural Network Tree (ANNT), i.e. ANN training preceded by Decision Tree rules extraction method is presented to overcome the comprehensibility problem of ANN. Two pruning techniques are used with the ANNT algorithm; one is to prune the neural network and another to prune the tree. Both of these pruning methods are evaluated to see the effect on ANNT in terms of accuracy, comprehensibility and fidelity.
This paper presents a deep learning approach to emotion recognition as applied to virtual reality and music predictive analytics. Firstly, it investigates the deep parameter tuning of the ...multi-hidden layer neural networks, which are also commonly referred to simply as deep networks that are used to conduct emotion detection in virtual reality (VR)- electroencephalography (EEG) predictive analytics. Deep networks have been studied extensively over the last decade and have shown to be among the most accurate methods for predictive analytics in image recognition and speech processing domains. However, most predictive analytics deep network studies focus on the shallow parameter tuning when attempting to boost prediction accuracies, which includes deep network tuning parameters such as number of hidden layers, number of hidden nodes per hidden layer and the types of activation functions used in the hidden nodes. Much less effort has been put into investigating the tuning of deep parameters such as input dropout ratios, L1 (lasso) regularization and L2 (ridge regularization) parameters of the deep networks. As such, the goal of this study is to perform a parameter tuning investigation on these deep parameters of the deep networks for predicting emotions in a virtual reality environment using electroencephalography (EEG) signal obtained when the user is exposed to immersive content. The results show that deep tuning of deep networks in VR-EEG can improve the accuracies of predicting emotions. The best emotion prediction accuracy was improved to over 96% after deep tuning was conducted on the deep network parameters of input dropout ratio, L1 and L2 regularization parameters. Secondly, it investigates a similar possible approach when applied to 4-quadrant music emotion recognition. Recent studies have been characterizing music based on music genres and various classification techniques have been used to achieve the best accuracy rate. Several researches on deep learning have shown outstanding results in relation to dimensional music emotion recognition. Yet, there is no concrete and concise description to express music. In regards to this research gap, a research using more detailed metadata on twodimensional emotion annotations based on the Russell’s model is conducted. Rather than applying music genres or lyrics into machine learning algorithm to MER, higher representation of music information, acoustic features are used. In conjunction with the four classes classification problem, an available dataset named AMG1608 is feed into a training model built from deep neural network. The dataset is first preprocessed to get full access of variables before any machine learning is done. The classification rate is then collected by running the scripts in R environment. The preliminary result showed a classification rate of 46.0%.
Eye-tracking technology has become popular recently and widely used in research on emotion recognition since its usability. In this paper, we presented a preliminary investigation on a novelty ...approach for detecting emotions using eye-tracking data in virtual reality (VR) to classify 4-quadrant of emotions according to russell’scircumplex model of affects. A presentation of 3600 videos is used as the experiment stimuli to evoke the emotions of the user in VR. An add-on eye-tracker within the VR headset is used for the recording and collecting device of eye-tracking data. Fixation data is extracted and chosen as the eye feature used in this investigation. The machine learning classifier is support vector machine (SVM) with radial basis function (RBF) kernel. The best classification accuracy achieved is 69.23%. The findings showed that emotion classification using fixation data has promising results in the prediction accuracy from a four-class random classification.
3D printing is an emerging trend fuelled by the rapid technology advancements in 3D printing technology. Printing out 3D designs is something new and interesting but the process of designing 3D ...objects is far from effortless. Researchers have recently forged ahead in conducting numerous studies on using mathematical formulas to create objects and shapes in 3D space. A mathematical encoding for geometric shapes called the Superformula was proposed by Johan Geilis through the generalization of the Supereclipse formula to generate 3D shapes and objects by extending its spherical products. The focus of this study is to investigate the ideal range of parametric values supplied to the Superformula in order to automatically generate 3D shapes and objects through the use of Evolution Algorithms (EAs). Thus, Evolutionary Programming was used as the EA in this study which serves as the main evolution component that uses a fitness function tailored in a way that it is able to evaluate the 3D objects and shapes generated by the Superformula. The values require by the Superformula to generate 3D objects or shapes are m_1,m_2,n_1,1,n_1,2,n_1,3,n_2,1,n_2,2,and n_2,3. To obtain the ideal range of values for the afore mentioned parameters, five different sets of experiments were carried out within the range set of {0 - 20}, {0 - 40}, {0 - 60}, {0 - 120}, and {0 - 240}.Each range set of numbers will be tested five times and the final objects from each of the runs were then analysed. From the observations obtained, the range set of {0- 20}, {0- 60}, and {0- 120} shows the most promising results as the final objects produced were unique and it was surmised that within these range of numbers contain highly unique and novel 3D objects and shapes.
This study investigates on aesthetics preference measurement of human using electroencephalogram (EEG) for virtual motion 3D shapes. The 3D shapes are generated using the Gielis superformula in ...bracelet-like shapes. EEG signals were collected by using a wireless medical grade EEG device, B-Alert X10 from Advance Brain Monitoring. Wavelet transforms were used to decompose the signals into 5 different bands, alpha, beta, gamma, delta and theta. Linear Discriminant analysis (LDA) and K-Nearest Neighbor (KNN) were used as classifiers to train and test different combinations of the features. Classification accuracy of up to 82.14% could be obtained using KNN with entropy of beta, gamma, delta and theta rhythms as features from channels Fz, POz and P4.
The proliferation of online auctions has caused the increasing need to monitor and track multiple bids in multiple auctions. As a solution to the problem, an autonomous agent was developed to work in ...a flexible and configurable heuristic decision making framework that can tackle the problem of bidding across multiple auctions that apply different protocols (English, Vickrey and Dutch). Due to the dynamic and unpredictable nature of online auctions, the agent utilizes genetic algorithm to search for effective solution. Instead of using the conventional genetic algorithm, this paper investigates the application of deterministic dynamic adaptation genetic algorithm and self adaptive genetic algorithm to search for the most effective strategies (offline). An empirical evaluation on the comparison between the effectiveness of self-adaptive genetic algorithm and deterministic dynamic adaptation genetic algorithm for searching the most effective strategies in the online auction environment are discussed in this paper.
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