Applied Computational Intelligence and Soft Computing http://www.hindawi.com The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures Tue, 15 Apr 2014 07:15:36 +0000 http://www.hindawi.com/journals/acisc/2014/197961/ Malware detection done at the network infrastructure level is still an open research problem ,considering the evolution of malwares and high detection accuracy needed to detect these threats. Content based classification techniques have been proven capable of detecting malware without matching for malware signatures. However, the performance of the classification techniques depends on observed training samples. In this paper, a new detection method that incorporates Snort malware signatures into Naive Bayes model training is proposed. Through experimental work, we prove that the proposed work results in low features search space for effective detection at the packet level. This paper also demonstrates the viability of detecting malware at the stateless level (using packets) as well as at the stateful level (using TCP byte stream). The result shows that it is feasible to detect malware at the stateless level with similar accuracy to the stateful level, thus requiring minimal resource for implementation on middleboxes. Stateless detection can give a better protection to end users by detecting malware on middleboxes without having to reconstruct stateful sessions and before malwares reach the end users. Ismahani Ismail, Sulaiman Mohd Nor, and Muhammad Nadzir Marsono Copyright © 2014 Ismahani Ismail et al. All rights reserved. Novel Adaptive Bacteria Foraging Algorithms for Global Optimization Tue, 25 Mar 2014 11:42:46 +0000 http://www.hindawi.com/journals/acisc/2014/494271/ This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system. Ahmad N. K. Nasir, M. O. Tokhi, and N. Maniha Abd. Ghani Copyright © 2014 Ahmad N. K. Nasir et al. All rights reserved. Feature Fusion Based Audio-Visual Speaker Identification Using Hidden Markov Model under Different Lighting Variations Wed, 05 Mar 2014 11:39:48 +0000 http://www.hindawi.com/journals/acisc/2014/831830/ The aim of the paper is to propose a feature fusion based Audio-Visual Speaker Identification (AVSI) system with varied conditions of illumination environments. Among the different fusion strategies, feature level fusion has been used for the proposed AVSI system where Hidden Markov Model (HMM) is used for learning and classification. Since the feature set contains richer information about the raw biometric data than any other levels, integration at feature level is expected to provide better authentication results. In this paper, both Mel Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) are combined to get the audio feature vectors and Active Shape Model (ASM) based appearance and shape facial features are concatenated to take the visual feature vectors. These combined audio and visual features are used for the feature-fusion. To reduce the dimension of the audio and visual feature vectors, Principal Component Analysis (PCA) method is used. The VALID audio-visual database is used to measure the performance of the proposed system where four different illumination levels of lighting conditions are considered. Experimental results focus on the significance of the proposed audio-visual speaker identification system with various combinations of audio and visual features. Md. Rabiul Islam and Md. Abdus Sobhan Copyright © 2014 Md. Rabiul Islam and Md. Abdus Sobhan. All rights reserved. Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features Wed, 05 Mar 2014 06:36:44 +0000 http://www.hindawi.com/journals/acisc/2014/415187/ People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images. Keranmu Xielifuguli, Akira Fujisawa, Yusuke Kusumoto, Kazuyuki Matsumoto, and Kenji Kita Copyright © 2014 Keranmu Xielifuguli et al. All rights reserved. Application of DEO Method to Solving Fuzzy Multiobjective Optimal Control Problem Thu, 27 Feb 2014 15:50:41 +0000 http://www.hindawi.com/journals/acisc/2014/971894/ In the present paper a problem of optimal control for a single-product dynamical macroeconomic model is considered. In this model gross domestic product is divided into productive consumption, gross investment, and nonproductive consumption. The model is described by a fuzzy differential equation (FDE) to take into account imprecision inherent in the dynamics that may be naturally conditioned by influence of various external factors, unforeseen contingencies of future, and so forth. The considered problems are characterized by four criteria and by several important aspects. On one hand, the problem is complicated by the presence of fuzzy uncertainty as a result of a natural imprecision inherent in information about dynamics of real-world systems. On the other hand, the number of the criteria is not small and most of them are integral criteria. Due to the above mentioned aspects, solving the considered problem by using convolution of criteria into one criterion would lead to loss of information and also would be counterintuitive and complex. We applied DEO (differential evolution optimization) method to solve the considered problem. Latafat A. Gardashova Copyright © 2014 Latafat A. Gardashova. All rights reserved. An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery Wed, 26 Feb 2014 06:36:30 +0000 http://www.hindawi.com/journals/acisc/2014/871412/ This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments. Shigeaki Sakurai, Kyoko Makino, and Shigeru Matsumoto Copyright © 2014 Shigeaki Sakurai et al. All rights reserved. Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges Thu, 20 Feb 2014 16:55:20 +0000 http://www.hindawi.com/journals/acisc/2014/735942/ Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification. Mita K. Dalal and Mukesh A. Zaveri Copyright © 2014 Mita K. Dalal and Mukesh A. Zaveri. All rights reserved. Assessment of Haar Wavelet-Quasilinearization Technique in Heat Convection-Radiation Equations Wed, 05 Feb 2014 14:43:01 +0000 http://www.hindawi.com/journals/acisc/2014/454231/ We showed that solutions by the Haar wavelet-quasilinearization technique for the two problems, namely, (i) temperature distribution equation in lumped system of combined convection-radiation in a slab made of materials with variable thermal conductivity and (ii) cooling of a lumped system by combined convection and radiation are strongly reliable and also more accurate than the other numerical methods and are in good agreement with exact solution. According to the Haar wavelet-quasilinearization technique, we convert the nonlinear heat transfer equation to linear discretized equation with the help of quasilinearization technique and apply the Haar wavelet method at each iteration of quasilinearization technique to get the solution. The main aim of present work is to show the reliability of the Haar wavelet-quasilinearization technique for heat transfer equations. Umer Saeed and Mujeeb ur Rehman Copyright © 2014 Umer Saeed and Mujeeb ur Rehman. All rights reserved. Subspace Clustering of High-Dimensional Data: An Evolutionary Approach Tue, 31 Dec 2013 17:12:32 +0000 http://www.hindawi.com/journals/acisc/2013/863146/ Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. In this paper, we have presented a robust multi objective subspace clustering (MOSCL) algorithm for the challenging problem of high-dimensional clustering. The first phase of MOSCL performs subspace relevance analysis by detecting dense and sparse regions with their locations in data set. After detection of dense regions it eliminates outliers. MOSCL discovers subspaces in dense regions of data set and produces subspace clusters. In thorough experiments on synthetic and real-world data sets, we demonstrate that MOSCL for subspace clustering is superior to PROCLUS clustering algorithm. Additionally we investigate the effects of first phase for detecting dense regions on the results of subspace clustering. Our results indicate that removing outliers improves the accuracy of subspace clustering. The clustering results are validated by clustering error (CE) distance on various data sets. MOSCL can discover the clusters in all subspaces with high quality, and the efficiency of MOSCL outperforms PROCLUS. Singh Vijendra and Sahoo Laxman Copyright © 2013 Singh Vijendra and Sahoo Laxman. All rights reserved. Crossover Method for Interactive Genetic Algorithms to Estimate Multimodal Preferences Tue, 31 Dec 2013 17:09:43 +0000 http://www.hindawi.com/journals/acisc/2013/302573/ We apply an interactive genetic algorithm (iGA) to generate product recommendations. iGAs search for a single optimum point based on a user’s Kansei through the interaction between the user and machine. However, especially in the domain of product recommendations, there may be numerous optimum points. Therefore, the purpose of this study is to develop a new iGA crossover method that concurrently searches for multiple optimum points for multiple user preferences. The proposed method estimates the locations of the optimum area by a clustering method and then searches for the maximum values of the area by a probabilistic model. To confirm the effectiveness of this method, two experiments were performed. In the first experiment, a pseudouser operated an experiment system that implemented the proposed and conventional methods and the solutions obtained were evaluated using a set of pseudomultiple preferences. With this experiment, we proved that when there are multiple preferences, the proposed method searches faster and more diversely than the conventional one. The second experiment was a subjective experiment. This experiment showed that the proposed method was able to search concurrently for more preferences when subjects had multiple preferences. Misato Tanaka, Yasunari Sasaki, Mitsunori Miki, and Tomoyuki Hiroyasu Copyright © 2013 Misato Tanaka et al. All rights reserved. An Algorithm for Extracting Intuitionistic Fuzzy Shortest Path in a Graph Mon, 02 Dec 2013 15:22:07 +0000 http://www.hindawi.com/journals/acisc/2013/970197/ We consider an intuitionistic fuzzy shortest path problem (IFSPP) in a directed graph where the weights of the links are intuitionistic fuzzy numbers. We develop a method to search for an intuitionistic fuzzy shortest path from a source node to a destination node. We coin the concept of classical Dijkstra’s algorithm which is applicable to graphs with crisp weights and then extend this concept to graphs where the weights of the arcs are intuitionistic fuzzy numbers. It is claimed that the method may play a major role in many application areas of computer science, communication network, transportation systems, and so forth. in particular to those networks for which the link weights (costs) are ill defined. Siddhartha Sankar Biswas, Bashir Alam, and M. N. Doja Copyright © 2013 Siddhartha Sankar Biswas et al. All rights reserved. Parallel Swarms Oriented Particle Swarm Optimization Tue, 05 Nov 2013 09:53:00 +0000 http://www.hindawi.com/journals/acisc/2013/756719/ The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO. Tad Gonsalves and Akira Egashira Copyright © 2013 Tad Gonsalves and Akira Egashira. All rights reserved. Numerical Solution of Uncertain Beam Equations Using Double Parametric Form of Fuzzy Numbers Sun, 20 Oct 2013 13:19:04 +0000 http://www.hindawi.com/journals/acisc/2013/764871/ Present paper proposes a new technique to solve uncertain beam equation using double parametric form of fuzzy numbers. Uncertainties appearing in the initial conditions are taken in terms of triangular fuzzy number. Using the single parametric form, the fuzzy beam equation is converted first to an interval-based fuzzy differential equation. Next, this differential equation is transformed to crisp form by applying double parametric form of fuzzy number. Finally, the same is solved by homotopy perturbation method (HPM) to obtain the uncertain responses subject to unit step and impulse loads. Obtained results are depicted in terms of plots to show the efficiency and powerfulness of the methodology. Smita Tapaswini and S. Chakraverty Copyright © 2013 Smita Tapaswini and S. Chakraverty. All rights reserved. Development of Product Data Model for Maintenance in Concrete Highway Bridges Tue, 27 Aug 2013 08:34:59 +0000 http://www.hindawi.com/journals/acisc/2013/148785/ The primary objective of this paper is to develop the product data models, in which systematic information is defined for accumulating, exchanging, and sharing in the maintenance of concrete highway bridges. The information requirement and existing issues and solutions were analyzed based on the life cycle and the standardization for sharing. The member data models and business data models that defined design and construction information and accumulated results information were developed. The maintenance business process in which project participants utilize the product data model was described as utilization scenario. The utilization frameworks which define information flow were developed. Satoshi Kubota and Ichizou Mikami Copyright © 2013 Satoshi Kubota and Ichizou Mikami. All rights reserved. Fuzzy Environmental Model for Evaluating Water Quality of Sangam Zone during Maha Kumbh 2013 Sun, 04 Aug 2013 14:11:22 +0000 http://www.hindawi.com/journals/acisc/2013/265924/ It is a well-known fact that water is the basic need of human beings. The industrial wastes nearby rivers and several anthropogenic activities are responsible for deteriorating water quality of rivers in India. The present research paper deals with the design and development of soft computing system to assess the water quality of rivers Ganga and Yamuna during the Maha Kumbh 2013 in and around Sangam Zone, Allahabad, by making use of physicochemical parameters relationship. Pankaj Srivastava, Anjali Burande, and Neeraja Sharma Copyright © 2013 Pankaj Srivastava et al. All rights reserved. A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification Wed, 24 Jul 2013 10:54:31 +0000 http://www.hindawi.com/journals/acisc/2013/515918/ Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches. Ujwalla Gawande, Mukesh Zaveri, and Avichal Kapur Copyright © 2013 Ujwalla Gawande et al. All rights reserved. Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews Tue, 16 Jul 2013 14:43:46 +0000 http://www.hindawi.com/journals/acisc/2013/910706/ The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users’ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%. Mita K. Dalal and Mukesh A. Zaveri Copyright © 2013 Mita K. Dalal and Mukesh A. Zaveri. All rights reserved. Use of Genetic Algorithm for Cohesive Summary Extraction to Assist Reading Difficulties Mon, 17 Jun 2013 11:19:43 +0000 http://www.hindawi.com/journals/acisc/2013/945623/ Learners with reading difficulties normally face significant challenges in understanding the text-based learning materials. In this regard, there is a need for an assistive summary to help such learners to approach the learning documents with minimal difficulty. An important issue in extractive summarization is to extract cohesive summary from the text. Existing summarization approaches focus mostly on informative sentences rather than cohesive sentences. We considered several existing features, including sentence location, cardinality, title similarity, and keywords to extract important sentences. Moreover, learner-dependent readability-related features such as average sentence length, percentage of trigger words, percentage of polysyllabic words, and percentage of noun entity occurrences are considered for the summarization purpose. The objective of this work is to extract the optimal combination of sentences that increase readability through sentence cohesion using genetic algorithm. The results show that the summary extraction using our proposed approach performs better in -measure, readability, and cohesion than the baseline approach (lead) and the corpus-based approach. The task-based evaluation shows the effect of summary assistive reading in enhancing readability on reading difficulties. K. Nandhini and S. R. Balasundaram Copyright © 2013 K. Nandhini and S. R. Balasundaram. All rights reserved. A New Multiphase Soft Segmentation with Adaptive Variants Thu, 23 May 2013 18:54:25 +0000 http://www.hindawi.com/journals/acisc/2013/921721/ Soft segmentation is more flexible than hard segmentation. But the membership functions are usually sensitive to noise. In this paper, we propose a multiphase soft segmentation model for nearly piecewise constant images based on stochastic principle, where pixel intensities are modeled as random variables with mixed Gaussian distribution. The novelty of this paper lies in three aspects. First, unlike some existing models where the mean of each phase is modeled as a constant and the variances for different phases are assumed to be the same, the mean for each phase in the Gaussian distribution in this paper is modeled as a product of a constant and a bias field, and different phases are assumed to have different variances, which makes the model more flexible. Second, we develop a bidirection projected primal dual hybrid gradient (PDHG) algorithm for iterations of membership functions. Third, we also develop a novel algorithm for explicitly computing the projection from to simplex for any dimension using dual theory, which is more efficient in both coding and implementation than existing projection methods. Hongyuan Wang, Fuhua Chen, and Yunmei Chen Copyright © 2013 Hongyuan Wang et al. All rights reserved. A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring Thu, 02 May 2013 08:47:10 +0000 http://www.hindawi.com/journals/acisc/2013/686345/ Improving energy efficiency by monitoring household electrical consumption is of significant importance with the climate change concerns of the present time. A solution for the electrical consumption management problem is the use of a nonintrusive appliance load monitoring (NIALM) system. This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched-on appliances. This paper focuses solely on feature extraction through applying the matrix pencil method, a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the current signal in terms of complex numbers referred to as poles and residues. These complex numbers are shown to be characteristic of the considered load and can thus serve as features in any subsequent classification module. In the absence of noise, simulations indicate an almost perfect agreement between theoretical and estimated values of poles and residues. For real data, poles and residues are used to determine a feature vector consisting of the contribution of the fundamental, the third, and the fifth harmonic currents to the maximum of the total load current. The result is a three-dimensional feature space with reduced intercluster overlap. Khaled Chahine and Khalil El Khamlichi Drissi Copyright © 2013 Khaled Chahine and Khalil El Khamlichi Drissi. All rights reserved. Argumentative SOX Compliant and Quality Decision Support Intelligent Expert System over the Suppliers Selection Process Sun, 28 Apr 2013 15:29:48 +0000 http://www.hindawi.com/journals/acisc/2013/973704/ The objective of this paper is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility and quality of the Suppliers Selection Process based on Artificial Intelligence and Argumentation Theory knowledge and techniques. The present SOX Law, in effect nowadays, was created to improve financial government control over US companies. This law is a factor standard out United States due to several factors like present globalization, expansion of US companies, or key influence of US stock exchange markets worldwide. This paper constitutes a novel approach to this kind of problems due to following elements: (1) it has an optimized structure to look for the solution, (2) it has a dynamic learning method to handle court and control gonvernment bodies decisions, (3) it uses fuzzy knowledge to improve its performance, and (4) it uses its past accumulated experience to let the system evolve far beyond its initial state. Jesus Angel Fernandez Canelas, Quintin Martin Martin, and Juan Manuel Corchado Rodriguez Copyright © 2013 Jesus Angel Fernandez Canelas et al. All rights reserved. Using Multicore Technologies to Speed Up Complex Simulations of Population Evolution Wed, 20 Mar 2013 11:04:00 +0000 http://www.hindawi.com/journals/acisc/2013/345297/ We explore with the use of multicore processing technologies for conducting simulations on population replacement of disease vectors. In our model, a native population of simulated vectors is inoculated with a small exogenous population of vectors that have been infected with the Wolbachia bacteria, which confers immunity to the disease. We conducted a series of computational simulations to study the conditions required by the invading population to take over the native population. Given the computational burden of this study, we decided to take advantage of modern multicore processor technologies for reducing the time required for the simulations. Overall, the results seem promising both in terms of the application and the use of multicore technologies. Mauricio Guevara-Souza and Edgar E. Vallejo Copyright © 2013 Mauricio Guevara-Souza and Edgar E. Vallejo. All rights reserved. Smartphone Homecare Monitoring of Hearts Tue, 05 Mar 2013 08:34:42 +0000 http://www.hindawi.com/journals/acisc/2013/983515/ Homecare monitoring blood pressures and heartbeats are commercially available using dedicated devices, for example, wrist watch, pulse oximetry. With the advent of Smartphone and compressive sensing technology, we wish to monitor precisely the electrical waveforms of heartbeats called the electrocardiography (ECG) for an aging global villager biomedical wellness homecare system. Our design separates into 3 innovative modules within the size-weight and power-cost bandwidth (Swap-CB) limitation. We develop each separately but in concert with one another: (i) Smart Electrode (adopting a low-power-mixed signal embedded with modern compressive sensing firmware and applying the nanotechnology to improve the electrodes’ contact impedance as well as novel transduction mechanism, between ECG and electronics, e.g., a pressure mattress coupling, or fiber-optics coupling); (ii) Learnable Database (utilizing adaptive wavelets transforms for systolic and diastolic P-QRS-T-U features extraction Aided Target Recognition and adopting Sequential Query Language for a relational database allowing distant monitoring and retrievable); (iii) Smartphone (inheriting a large touch screen interface display with powerful computation capability and assisting caretaker reporting system with GPS and ID and two-way interaction with patient panic button for programmable emergence reporting procedure). While (i) is novel, (ii) and (iii) are mature. Together, they can eventually provide a supplementary home screening system for the post- or the prediagnosis care at home with a built-in database searchable with the time, the place, and the degree of urgency happened, using in situ screening. Harold Szu, Charles Hsu, Gyu Moon, Joseph Landa, Hiroshi Nakajima, and Yutaka Hata Copyright © 2013 Harold Szu et al. All rights reserved. On the Variability of Neural Network Classification Measures in the Protein Secondary Structure Prediction Problem Thu, 31 Jan 2013 16:12:45 +0000 http://www.hindawi.com/journals/acisc/2013/794350/ We revisit the protein secondary structure prediction problem using linear and backpropagation neural network architectures commonly applied in the literature. In this context, neural network mappings are constructed between protein training set sequences and their assigned structure classes in order to analyze the class membership of test data and associated measures of significance. We present numerical results demonstrating that classifier performance measures can vary significantly depending upon the classifier architecture and the structure class encoding technique. Furthermore, an analytic formulation is introduced in order to substantiate the observed numerical data. Finally, we analyze and discuss the ability of the neural network to accurately model fundamental attributes of protein secondary structure. Eric Sakk and Ayanna Alexander Copyright © 2013 Eric Sakk and Ayanna Alexander. All rights reserved. Smartphone Household Wireless Electroencephalogram Hat Wed, 30 Jan 2013 14:24:31 +0000 http://www.hindawi.com/journals/acisc/2013/241489/ Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of active among passive electrodes to form -organized random linear combinations of readouts, . Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences). Harold Szu, Charles Hsu, Gyu Moon, Takeshi Yamakawa, Binh Q. Tran, Tzyy Ping Jung, and Joseph Landa Copyright © 2013 Harold Szu et al. All rights reserved. Public Project Portfolio Optimization under a Participatory Paradigm Wed, 30 Jan 2013 09:19:03 +0000 http://www.hindawi.com/journals/acisc/2013/891781/ A new democracy paradigm is emerging through participatory budgeting exercises, which can be defined as a public space in which the government and the society agree on how to adapt the priorities of the citizenship to the public policy agenda. Although these priorities have been identified and they are likely to be reflected in a ranking of public policy actions, there is still a challenge of solving a portfolio problem of public projects that should implement the agreed agenda. This work proposes two procedures for optimizing the portfolio of public actions with the information stemming from the citizen participatory exercise. The selection of the method depends on the information about preferences collected from the participatory group. When the information is sufficient, the method behaves as an instrument of legitimate democracy. The proposal performs very well in solving two real-size examples. Eduardo Fernandez and Rafael Olmedo Copyright © 2013 Eduardo Fernandez and Rafael Olmedo. All rights reserved. A Nanotechnology Enhancement to Moore's Law Mon, 28 Jan 2013 13:38:54 +0000 http://www.hindawi.com/journals/acisc/2013/426962/ Intel Moore observed an exponential doubling in the number of transistors in every 18 months through the size reduction of transistor components since 1965. In viewing of mobile computing with insatiate appetite, we explored the necessary enhancement by an increasingly maturing nanotechnology and facing the inevitable quantum-mechanical atomic and nuclei limits. Since we cannot break down the atomic size barrier, the fact implies a fundamental size limit at the atomic/nucleus scale. This means, no more simple 18-month doubling, but other forms of transistor doubling may happen at a different slope. We are particularly interested in the nano enhancement area. (i) 3 Dimensions: If the progress in shrinking the in-plane dimensions is to slow down, vertical integration can help increasing the areal device transistor density. As the devices continue to shrink into the 20 to 30 nm range, the consideration of thermal properties and transport in such devices becomes increasingly important. (ii) Quantum computing: The other types of transistor material are rapidly developed in laboratories worldwide, for example, Spintronics, Nanostorage, HP display Nanotechnology, which are modifying this Law. We shall consider the limitation of phonon engineering fundamental information unit “Qubyte” in quantum computing, Nano/Micro Electrical Mechanical System (NEMS), Carbon Nanotubes, single-layer Graphenes, single-strip Nano-Ribbons, and so forth. Jerry Wu, Yin-Lin Shen, Kitt Reinhardt, Harold Szu, and Boqun Dong Copyright © 2013 Jerry Wu et al. All rights reserved. Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework Mon, 31 Dec 2012 13:28:39 +0000 http://www.hindawi.com/journals/acisc/2012/871324/ In spoken word recognition, one of the crucial points is to identify the vowel phonemes. This paper describes an Artificial Neural Network (ANN) based algorithm developed for the segmentation and recognition of the vowel phonemes of Assamese language from some words containing those vowels. Self-Organizing Map (SOM) trained with a various number of iterations is used to segment the word into its constituent phonemes. Later, Probabilistic Neural Network (PNN) trained with clean vowel phonemes is used to recognize the vowel segment from the six different SOM segmented phonemes. One of the important aspects of the proposed algorithm is that it proves the validation of the recognized vowel by checking its first formant frequency. The first formant frequency of all the Assamese vowels is predetermined by estimating pole or formant location from the linear prediction (LP) model of the vocal tract. The proposed algorithm shows a high recognition performance in comparison to the conventional Discrete Wavelet Transform (DWT) based segmentation. Mousmita Sarma and Kandarpa Kumar Sarma Copyright © 2012 Mousmita Sarma and Kandarpa Kumar Sarma. All rights reserved. Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition Mon, 24 Dec 2012 12:36:59 +0000 http://www.hindawi.com/journals/acisc/2012/871834/ In handwritten character recognition, benchmark database plays an important role in evaluating the performance of various algorithms and the results obtained by various researchers. In Devnagari script, there is lack of such official benchmark. This paper focuses on the generation of offline benchmark database for Devnagari handwritten numerals and characters. The present work generated 5137 and 20305 isolated samples for numeral and character database, respectively, from 750 writers of all ages, sex, education, and profession. The offline sample images are stored in TIFF image format as it occupies less memory. Also, the data is presented in binary level so that memory requirement is further reduced. It will facilitate research on handwriting recognition of Devnagari script through free access to the researchers. Vikas J. Dongre and Vijay H. Mankar Copyright © 2012 Vikas J. Dongre and Vijay H. Mankar. All rights reserved. Neural Behavior Chain Learning of Mobile Robot Actions Thu, 06 Dec 2012 11:37:00 +0000 http://www.hindawi.com/journals/acisc/2012/382782/ This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance. Lejla Banjanovic-Mehmedovic, Dzenisan Golic, Fahrudin Mehmedovic, and Jasna Havic Copyright © 2012 Lejla Banjanovic-Mehmedovic et al. All rights reserved.