Search results for: Random Forest Classification

Authors: M. M. Qasaymeh, M. A. Khodeir

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Subspace channel estimation methods have been studied widely, where the subspace of the covariance matrix is decomposed to separate the signal subspace from noise subspace. The decomposition is normally done by using either the eigenvalue decomposition (EVD) or the singular value decomposition (SVD) of the auto-correlation matrix (ACM). However, the subspace decomposition process is computationally expensive. This paper considers the estimation of the multipath slow frequency hopping (FH) channel using noise space based method. In particular, an efficient method is proposed to estimate the multipath time delays by applying multiple signal classification (MUSIC) algorithm which is based on the null space extracted by the rank revealing LU (RRLU) factorization. As a result, precise information is provided by the RRLU about the numerical null space and the rank, (i.e., important tool in linear algebra). The simulation results demonstrate the effectiveness of the proposed novel method by approximately decreasing the computational complexity to the half as compared with RRQR methods keeping the same performance.

Keywords: Time Delay Estimation, RRLU, RRQR, MUSIC, LS-ESPRIT, LS-ESPRIT, Frequency Hopping.

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Authors: Pranee Tridhoskul

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This research paper is aimed to examine a relationship between the service marketing mix and customers’ frequency of use of service at Mercedes Benz Auto Repair Centres under Thonburi Group, Thailand. Based on 2,267 customers who used the service of Thonburi Group’s Auto Repair Centres as the population, the sampling of this research was a total of 340 samples, by use of Probability Sampling Technique. Systematic Random Sampling was applied by use of questionnaire in collecting the data at Thonburi Group’s Auto Repair Centres. Mean and Pearson’s basic statistical correlations were utilized in analyzing the data. The study discovered a medium level of customers’ perception towards product and service of Thonburi Group’s Auto Repair Centres, price, place or distribution channel and promotion. People who provided service were perceived also at a medium level, whereas the physical evidence and service process were perceived at a high level. Furthermore, there appeared a correlation between the physical evidence and service process, and customers’ frequency of use of automobile service per year.

Keywords: Service Marketing Mix, Behavior, Mercedes Auto Service Centre.

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Authors: Dewan Md. Farid, Nguyen Huu Hoa, Jerome Darmont, Nouria Harbi, Mohammad Zahidur Rahman

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In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and dynamic large intrusion detection dataset, the detection accuracy of naïve Bayesian classifier does not scale up as well as decision tree. It has been successfully tested in other problem domains that naïve Bayesian tree improves the classification rates in large dataset. In naïve Bayesian tree nodes contain and split as regular decision-trees, but the leaves contain naïve Bayesian classifiers. The experimental results on KDD99 benchmark network intrusion detection dataset demonstrate that this new approach scales up the detection rates for different attack types and reduces false positives in network intrusion detection.

Keywords: Detection rates, false positives, network intrusiondetection, naïve Bayesian tree.

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Authors: Mahanijah Md Kamal., Dingli Yu

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This paper presents a new method of fault detection and isolation (FDI) for polymer electrolyte membrane (PEM) fuel cell (FC) dynamic systems under an open-loop scheme. This method uses a radial basis function (RBF) neural network to perform fault identification, classification and isolation. The novelty is that the RBF model of independent mode is used to predict the future outputs of the FC stack. One actuator fault, one component fault and three sensor faults have been introduced to the PEMFC systems experience faults between -7% to +10% of fault size in real-time operation. To validate the results, a benchmark model developed by Michigan University is used in the simulation to investigate the effect of these five faults. The developed independent RBF model is tested on MATLAB R2009a/Simulink environment. The simulation results confirm the effectiveness of the proposed method for FDI under an open-loop condition. By using this method, the RBF networks able to detect and isolate all five faults accordingly and accurately.

Keywords: Polymer electrolyte membrane fuel cell, radial basis function neural networks, fault detection, fault isolation.

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Authors: Jafar Ahmadi, Alireza Pour-Aboughadareh

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Wheat is the first and the most important grain of the world and its bakery property is due to glutenin and gliadin qualities. Wheat seed proteins were divided into four groups according to solubility including albumin, globulin, glutenin and prolamin or gliadin. Gliadins are major components of the storage proteins in wheat endosperm. It seems that little information is available about gliadin genes in Iranian wild relatives of wheat. Thus, the aim of this study was the evaluation of the wheat wild relatives collected from different origins of Zagros Mountains in Iran, in terms of coding gliadin genes using specific primers. For this, forty accessions of Triticum boeoticum and Triticum urartu were selected for this study. For each accession, genomic DNA was extracted and PCRs were performed in total volumes of 15 μl. The amplification products were separated on 1.5% agarose gels. In results, for Gli-2A locus three allelic variants were detected by Gli-2As primer pairs. The sizes of PCR products for these alleles were 210, 490 and 700 bp. Only five (13%) and two accessions (5%) produced 700 and 490 bp fragments when their DNA was amplified with the Gli.As.2 primer pairs. However, 93% of the accessions carried allele 210 bp, and only 8% did not any product for this marker. Therefore, these germplasm could be used as rich gene pool to broaden the genetic base of bread wheat.

Keywords: Diploied wheat, gliadin, Triticum boeoticum, Triticum urartu.

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Authors: Hiroshi Hirano, Joji Kadota, Toshiyuki Yamashita, Yasuyuki Agari

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It is well known that enhancing interfacial adhesion between inorganic filler and matrix resin in a composite lead to favorable properties such as excellent mechanical properties, high thermal resistance, prominent electric insulation, low expansion coefficient, and so on. But it should be avoided that much excess of coupling agent is reacted due to a negative impact of their final composite-s properties. There is no report to achieve classification of the bonding state excepting investigation of coating layer thickness. Therefore, the analysis of the bonding state of the coupling agent reacted with the filler surface such as BN particles with less functional group and silica particles having much functional group was performed by thermal gravimetric analysis and pyrolysis GC/MS. The reacted number of functional groups on the silane-coupling agent was classified as a result of the analysis. Thus, we succeeded in classifying the reacted number of the functional groups as a result of this study.

Keywords: Inorganic filler, boron nitride, surface treatment, coupling agent, analysis of bonding state

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Authors: Jonathan D. Morales M., Ramón Silva R.

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The purpose of this project is to carry out an analysis and determine the profile of actual lean manufacturing processes in the Metropolitan Area of Bucaramanga. Through the analysis of qualitative and quantitative variables it was possible to establish how these manufacturers develop production practices that ensure their competitiveness and productivity in the market. In this study, a random sample of metallurgic and wrought iron companies was applied, following which a quantitative focus and analysis was used to formulate a qualitative methodology for measuring the level of lean manufacturing procedures in the industry. A qualitative evaluation was also carried out through a multivariate analysis using the Numerical Taxonomy System (NTSYS) program which should allow for the determination of Lean Manufacturing profiles. Through the results it was possible to observe how the companies in the sector are doing with respect to Lean Manufacturing Practices, as well as identify the level of management that these companies practice with respect to this topic. In addition, it was possible to ascertain that there is no one dominant profile in the sector when it comes to Lean Manufacturing. It was established that the companies in the metallurgic and wrought iron industry show low levels of Lean Manufacturing implementation. Each one carries out diverse actions that are insufficient to consolidate a sectoral strategy for developing a competitive advantage which enables them to tie together a production strategy.

Keywords: Lean manufacturing, metallurgic industry, production line management, productivity.

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Authors: I. M. Mungadi, M. S. Argungu, N. I. Mahmud

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This research aimed at developing a web-based mobile system and figuring out a better understanding of how could “web-based mobile system supports farmers in Kebbi State”. Thus, by finding out the answers to the research questions, a conceptual framework of the entire system was implemented using Unified Modelling Language (UML). The work involved a review of existing research on web-based mobile technology for farmers in some countries and other geographical areas within Nigeria. This research explored how farmers in Northern Nigeria, especially in Kebbi state, make use of the web-based mobile system for agribusiness. Also, the benefits of using web-based mobile systems and the challenges farmers face using such systems were examined. Considering the dynamic nature of theory of information and communication technology; this research employed survey and focus group discussion (FGD) methods. Stratified, random, purposive, and convenience sampling techniques were adopted to select the sample. A questionnaire and FGD guide were used to collect data. The survey finds that most of the Kebbi state farms use their alternative medium to get relevant information for their agribusiness. Also, the research reveals that using a web-based mobile system can benefit farmers significantly. Finally, the study has successfully developed and implemented the proposed system using mobile technology in addition to the framework design.

Keywords: Agribusiness, farmers, Kebbi State, mobile technology, Northern Nigeria, web-based.

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Authors: Hachaichi Mohamed Nour El-Islem, Baouni Tahar

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Environmental sustainability rather than a trans-disciplinary and a scientific issue, is the main problem that characterizes all modern cities nowadays. In developing countries, this concern is expressed in a plethora of critical urban ills: traffic congestion, air pollution, noise, urban decay, increase in energy consumption and CO₂ emissions which blemish cities’ landscape and might threaten citizens’ health and welfare. As in the same manner as developing world cities, the rapid growth of Algiers’ human population and increasing in city scale phenomena lead eventually to increase in daily trips, energy consumption and CO₂ emissions. In addition, the lack of proper and sustainable planning of the city’s infrastructure is one of the most relevant issues from which Algiers suffers. The aim of this contribution is to estimate the carbon deficit of the City of Algiers, Algeria, using the Ecological Footprint Model (carbon footprint). In order to achieve this goal, the amount of CO₂ from fuel combustion has been calculated and aggregated into five sectors (agriculture, industry, residential, tertiary and transportation); as well, Algiers’ biocapacity (CO₂ uptake land) has been calculated to determine the ecological overshoot. This study shows that Algiers’ transport system is not sustainable and is generating more than 50% of Algiers total carbon footprint which cannot be sequestered by the local forest land. The aim of this research is to show that the Carbon Footprint Assessment might be a relevant indicator to design sustainable strategies/policies striving to reduce CO₂ by setting in motion the energy consumption in the transportation sector and reducing the use of fossil fuels as the main energy input.

Keywords: Biocapacity, carbon footprint, ecological footprint assessment, energy consumption.

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Authors: K. Shiba, T. Kaburagi, Y. Kurihara

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With global aging, people who require care, such as people with dementia (PwD), are increasing within many developed countries. And PwDs may wander and unconsciously set foot outdoors, it may lead serious accidents, such as, traffic accidents. Here, round-the-clock monitoring by caregivers is necessary, which can be a burden for the caregivers. Therefore, an automatic wandering detection system is required when an elderly person wanders outdoors, in which case the detection system transmits a ‘moving’ followed by an ‘absence’ state. In this paper, we focus on the transition from the ‘resting’ to the ‘absence’ state, via the ‘moving’ state as one of the wandering transitions. To capture the transition of the three states, our method based on the hidden Markov model (HMM) is built. Using our method, the restraint where the ‘resting’ state and ‘absence’ state cannot be transmitted to each other is applied. To validate our method, we conducted the experiment with 10 subjects. Our results show that the method can classify three states with 0.92 accuracy.

Keywords: Wander, microwave Doppler sensor, respiratory frequency band, the state transition, hidden Markov model.

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Authors: Myungraee Cha, Jun Seok Kim, Seung Hwan Park, Jun-Geol Baek

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In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.

Keywords: Density estimation, Multivariate control chart, Oneclass classification, Support vector data description (SVDD)

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Authors: Nguyen Minh, Tuan, Chung-Ruey, Yen, Bui Lan, Anh

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The study was carried out to evaluated effect of S-gridling on fruit growth and quality of wax apple. The study was laid in Random completed block design with four replicated. Four treatment were applied as follows: S-girdling, fruit thinning plus bagging with 2,4-D sprayed, fruit thinning plus bagging and the control treatment. 2,4D was sprayed at the small bud and petal fall stage. Girdling was applied three week before flowering. The effect of all treatments on fruit growth was measured weekly. Number of flower, fruit set, fruit drop, fruit crack, and fruit quality were recorded. The result indicated that S-girdling, 2,4D application produced the lowest bud drop, fruit drop compared to untreated control. S-girdling improved faster fruit growth producing the best final fruit length and diameter compared to untreated control. S-girdling also markedly enhanced fruit set, fruit weight, and total soluble solid, reduced fruit crack, titratable acidity. On the other hand, it was noticed that with 2,4-D application also increased the fruit growth rate, improved physiological and biochemical characters of fruit than control treatment. It was concluded that S-girdling was recommended as the industry norm to increase fruit set, fruit quality in wax apple. 2,4D application had a distinctive and significant effect on most of the fruit quality characteristics assessed.

Keywords: S-girdling, 2, 4D, wax apple, fruit growth, fruit quality.

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Authors: Siri-Orn Champatong

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This research paper was aimed to examine the relationship between visitors’ attitude towards the service marketing mix and visitors’ frequency of visit to Bangpu Recreation Centre. Based on a large and uncalculated population, the number of samples was calculated according to the formula to obtain a total of 385 samples. In collecting the samples, systematic random sampling was applied and by using of a Likert five-scale questionnaire for, a total of 21 days to collect the needed information. Mean, Standard Deviation, and Pearson’s basic statistical correlations were utilized in analyzing the data. This study discovered a high level of visitors’ attitude product and service of Bangpu Recreation Centre, price, place, promotional activities, people who provided service and physical evidence of the centre. The attitude towards process of service was discovered to be at a medium level. Additionally, the finding of an examination of a relationship between visitors’ attitude towards service marketing mix and visitors’ frequency of visit to Bangpu Recreation Centre presented that product and service, people, physical evidence and process of service provision showed a relationship with the visitors’ frequency of visit to the centre per year.

Keywords: Frequency of Visit, Visitor, Service Marketing Mix, Bangpu Recreation Centre.

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks, bit-serial neural processor, FPGA, Neural Processing Element.

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple factorial correspondence analysis, principal component analysis, factor analysis, E.U.-28 countries, statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu statistics.

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Authors: Abegunde Linda, Adedeji Oluwatola, Tope-Ajayi Opeyemi

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Maize constitutes a major agrarian production for use by the vast population but despite its economic importance; it has not been produced to meet the economic needs of the country. Achieving optimum yield in maize can meaningfully be supported by land suitability analysis in order to guarantee self-sufficiency for future production optimization. This study examines land suitability for maize production through the analysis of the physicochemical variations in soil properties and other land attributes over space using a Geographic Information System (GIS) framework. Physicochemical parameters of importance selected include slope, landuse, physical and chemical properties of the soil, and climatic variables. Landsat imagery was used to categorize the landuse, Shuttle Radar Topographic Mapping (SRTM) generated the slope and soil samples were analyzed for its physical and chemical components. Suitability was categorized into highly, moderately and marginally suitable based on Food and Agricultural Organisation (FAO) classification, using the Analytical Hierarchy Process (AHP) technique of GIS. This result can be used by small scale farmers for efficient decision making in the allocation of land for maize production.

Keywords: AHP, GIS, MCE, Suitability, Zea mays.

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Authors: Mohammed Rziza, Mohamed El Aroussi, Mohammed El Hassouni, Sanaa Ghouzali, Driss Aboutajdine

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In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA, and independent component Analysis (ICA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet-LDA algorithm. Experimental results on ORL, YALE and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Keywords: Curvelet, Linear Discriminant Analysis (LDA) , Contourlet, Discreet Wavelet Transform, DWT, Block-based analysis, face recognition (FR).

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Authors: Kittisak Kerdprasop, Nittaya Kerdprasop, Pairote Sattayatham

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Data stream analysis is the process of computing various summaries and derived values from large amounts of data which are continuously generated at a rapid rate. The nature of a stream does not allow a revisit on each data element. Furthermore, data processing must be fast to produce timely analysis results. These requirements impose constraints on the design of the algorithms to balance correctness against timely responses. Several techniques have been proposed over the past few years to address these challenges. These techniques can be categorized as either dataoriented or task-oriented. The data-oriented approach analyzes a subset of data or a smaller transformed representation, whereas taskoriented scheme solves the problem directly via approximation techniques. We propose a hybrid approach to tackle the data stream analysis problem. The data stream has been both statistically transformed to a smaller size and computationally approximated its characteristics. We adopt a Monte Carlo method in the approximation step. The data reduction has been performed horizontally and vertically through our EMR sampling method. The proposed method is analyzed by a series of experiments. We apply our algorithm on clustering and classification tasks to evaluate the utility of our approach.

Keywords: Data Stream, Monte Carlo, Sampling, DensityEstimation.

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Authors: Panupong Chanplin, Kathaleeya Chanda., Wilailuk Mepracha

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The aims of this research were twofold: 1) to brand identity design of Creative Agriculture Community Enterprise, Bang Khonthi District, Samut Songkhram Province and 2) to study the level of tourist satisfaction towards brand identity design of Creative Agriculture Community Enterprise, Bang Khonthi District, Samut Songkhram Province. tourist satisfaction was measured using six criteria: clear brand positioning, likeable brand personality, memorable logo, attractive color palette, professional typography and on-brand supporting graphics. The researcher utilized a probability sampling method via simple random sampling. The sample consisted of 30 tourists in the Creative Agriculture Community Enterprise. Statistics utilized for data analysis were percentage, mean, and standard deviation. The results suggest that tourist had high levels of satisfaction towards all six criteria of the brand identity design that was designed to target them. This study proposes that specifically brand identity designed of Creative Agriculture Community Enterprise could also be implemented with other real media already available on the market.

Keywords: Satisfaction, brand identity, logo, creative agriculture community enterprise.

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Authors: W. Y. Leng, S. M. Shamsuddin

Abstract:

Fingerprint based identification system; one of a well known biometric system in the area of pattern recognition and has always been under study through its important role in forensic science that could help government criminal justice community. In this paper, we proposed an identification framework of individuals by means of fingerprint. Different from the most conventional fingerprint identification frameworks the extracted Geometrical element features (GEFs) will go through a Discretization process. The intention of Discretization in this study is to attain individual unique features that could reflect the individual varianceness in order to discriminate one person from another. Previously, Discretization has been shown a particularly efficient identification on English handwriting with accuracy of 99.9% and on discrimination of twins- handwriting with accuracy of 98%. Due to its high discriminative power, this method is adopted into this framework as an independent based method to seek for the accuracy of fingerprint identification. Finally the experimental result shows that the accuracy rate of identification of the proposed system using Discretization is 100% for FVC2000, 93% for FVC2002 and 89.7% for FVC2004 which is much better than the conventional or the existing fingerprint identification system (72% for FVC2000, 26% for FVC2002 and 32.8% for FVC2004). The result indicates that Discretization approach manages to boost up the classification effectively, and therefore prove to be suitable for other biometric features besides handwriting and fingerprint.

Keywords: Discretization, fingerprint identification, geometrical features, pattern recognition

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Authors: Qazi Umer Jamil, Abubakr Siddique, Mubeen Ur Rehman, Nida Aziz, Mohsin I. Tiwana

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This paper discusses a brain controlled robotic gait trainer for neurorehabilitation of Spinal Cord Injury (SCI) patients. Patients suffering from Spinal Cord Injuries (SCI) become unable to execute motion control of their lower proximities due to degeneration of spinal cord neurons. The presented approach can help SCI patients in neuro-rehabilitation training by directly translating patient motor imagery into walkers motion commands and thus bypassing spinal cord neurons completely. A non-invasive EEG based brain-computer interface is used for capturing patient neural activity. For signal processing and classification, an open source software (OpenVibe) is used. Classifiers categorize the patient motor imagery (MI) into a specific set of commands that are further translated into walker motion commands. The robotic walker also employs fall detection for ensuring safety of patient during gait training and can act as a support for SCI patients. The gait trainer is tested with subjects, and satisfactory results were achieved.

Keywords: Brain Computer Interface (BCI), gait trainer, Spinal Cord Injury (SCI), neurorehabilitation.

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Authors: Mohammad Bakhshi, Firas Al Janabi

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High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.

Keywords: DiMoN tool, disaggregation, exceedance probability, Kolmogorov-Smirnov Test, rainfall.

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Authors: M. H. Sedaghat, A. Zamani, S. Morshedi, R. Janamiri, M. Safdari, I. Mahdavi, A. Hosseini, A. Hatampour

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Although lots of experiments have been done in enhanced oil recovery, the number of experiments which consider the effects of local and global heterogeneity on efficiency of enhanced oil recovery based on the polymer-surfactant flooding is low and rarely done. In this research, we have done numerous experiments of water flooding and polymer-surfactant flooding on a five spot glass micromodel in different conditions such as different positions of layers. In these experiments, five different micromodels with three different pore structures are designed. Three models with different layer orientation, one homogenous model and one heterogeneous model are designed. In order to import the effect of heterogeneity of porous media, three types of pore structures are distributed accidentally and with equal ratio throughout heterogeneous micromodel network according to random normal distribution. The results show that maximum EOR recovery factor will happen in a situation where the layers are orthogonal to the path of mainstream and the minimum EOR recovery factor will happen in a situation where the model is heterogeneous. This experiments show that in polymer-surfactant flooding, with increase of angles of layers the EOR recovery factor will increase and this recovery factor is strongly affected by local heterogeneity around the injection zone.

Keywords: Layered Reservoir, Micromodel, Local Heterogeneity, Polymer-Surfactant Flooding, Enhanced Oil Recovery.

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

Keywords: Iot, activity recognition, automatic classification, unconstrained environment, deep neural networks.

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an  enormous number of applications, cyber-threats have significantly  increased accordingly. Thus, accurate detection of malicious traffic in  a timely manner is a critical concern in today’s Internet for security.  One approach for intrusion detection is to use Machine Learning (ML)  techniques. Several methods based on ML algorithms have been  introduced over the past years, but they are largely limited in terms of  detection accuracy and/or time and space complexity to run. In this  work, we present a novel method for intrusion detection that  incorporates a set of supervised learning algorithms. The proposed  technique provides high accuracy and outperforms existing techniques  that simply utilizes a single learning method. In addition, our  technique relies on partial flow information (rather than full  information) for detection, and thus, it is light-weight and desirable for  online operations with the property of early identification. With the  mid-Atlantic CCDC intrusion dataset publicly available, we show that  our proposed technique yields a high degree of detection rate over 99%  with a very low false alarm rate (0.4%). 

 

Keywords: Intrusion Detection, Supervised Learning, Traffic Classification.

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Authors: L. K. Davis

Abstract:

The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.

Keywords: 14-3-3 docking genes, synthetic protein design, time based DNA codes, writing DNA code from scratch.

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Authors: Muntanavadee Maytapattana

Abstract:

The purposes of the study were to evaluate the effectiveness of the Parent Training Program for Obesity Reduction (PTPOR) on health behaviors of school-age children. An Ecological Systems Theory (EST) was approached the study and a randomized control trial was used in this study. Participants were school-age overweight or obese children and their parents. One hundred and one parent-child dyads were recruited and random assigned into the PTPOR (N=30), Educational Intervention or EI (N=32), and control group (N=39). The parents in the PTPOR group participated in five sessions including an educational session, a cooking session, aerobic exercise training, 2-time group discussion sessions, and 4-time telephoned counseling sessions. Repeated Measure ANCOVA was used to analyze data. The results presented that the outcomes of the PTPOR group were better than the EI and the control groups at 1^st, 8^th, and 32^nd weeks after finishing the program such as child exercise behavior (F_(2,97) = 3.98, p = .02) and child dietary behavior (F_(2,97) = 9.42, p = .00). The results suggest that nurses and health care providers should utilize the PTPOR for child weight reduction and for the health promotion of a lifestyle among overweight and obese children.

Keywords: Parent training program for obesity reduction, child health behaviors, school-age children.

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

Abstract:

Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: Data mining, hybrid storage system, recurrent neural network, support vector machine.

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Authors: D. R. Delgado Sobrino, P. Košťál, J. Oravcová

Abstract:

Lately, significant work in the area of Intelligent Manufacturing has become public and mainly applied within the frame of industrial purposes. Special efforts have been made in the implementation of new technologies, management and control systems, among many others which have all evolved the field. Aware of all this and due to the scope of new projects and the need of turning the existing flexible ideas into more autonomous and intelligent ones, i.e.: Intelligent Manufacturing, the present paper emerges with the main aim of contributing to the design and analysis of the material flow in either systems, cells or work stations under this new “intelligent" denomination. For this, besides offering a conceptual basis in some of the key points to be taken into account and some general principles to consider in the design and analysis of the material flow, also some tips on how to define other possible alternative material flow scenarios and a classification of the states a system, cell or workstation are offered as well. All this is done with the intentions of relating it with the use of simulation tools, for which these have been briefly addressed with a special focus on the Witness simulation package. For a better comprehension, the previous elements are supported by a detailed layout, other figures and a few expressions which could help obtaining necessary data. Such data and others will be used in the future, when simulating the scenarios in the search of the best material flow configurations.

Keywords: Flexible/Intelligent Manufacturing System/Cell (F/IMS/C), material flow/design/configuration (MF/D/C), workstation.

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Authors: K. Mushtaq, M. Umar

Abstract:

The research aims to study the association between job satisfaction, motivation and the five factors of organizational citizenship behavior (i.e. Altruism, Conscientiousness, Sportsmanship, Courtesy and Civic virtue) among Public Sector Employees in Pakistan. In this research Structure Equation Modeling with confirmatory factor analysis was used to test the relationship between two independent and five dependent variables. Data was collected through questionnaire survey from 152 Public Servants Working in Gujrat District-Pakistan in different capacities. Stratified Random Sampling Technique was used to conduct this survey. The results of the study indicate that five factors of OCB have positive significant relation with both motivation and job satisfaction except the relationship of Civic Virtue with Motivation. The research findings implicate that factors other than motivation and job satisfaction may also affect OCB. Likewise, all the five factors of OCB may not be present in all populations. Thus, Managers must concentrate on increasing motivation and job satisfaction to increase OCB. Furthermore, the present research gives a direction to future researchers to use more independent variables (e.g. Culture, leadership, workplace environment, various job attitudes, types of motivation, etc.) on different types of populations with larger sample size in order to find the reasons behind insignificant relationship of civic virtue with Motivation in the research in hand and to generalize the tested model.

Keywords: Five Factors of Organizational Citizenship Behavior (OCB), Motivation, Job Satisfaction, Public Sector Employees in Pakistan.

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