Search results for: Training stress

Authors: A. Bencsik, - A. Sólyom

Abstract:

The role of knowledge is a determinative factor in the life of economy and society. To determine knowledge is not an easy task yet the real task is to determine the right knowledge. From this view knowledge is a sum of experience, ideas and cognitions which can help companies to remain in markets and to realize a maximum profit. At the same time changes of circumstances project in advance that contents and demands of the right knowledge are changing. In this paper we will analyse a special segment on the basis of an empirical survey. We investigated the behaviour and strategies of small and medium sized enterprises (SMEs) in the area of knowledge-handling. This survey was realized by questionnaires and wide range statistical methods were used during processing. As a result we will show how these companies are prepared to operate in a knowledge-based economy and in which areas they have prominent deficiencies.

Keywords: education, knowledge, knowledgemanagement, strategy, SME

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Authors: Masoud Hekmatpanah, Mohammad Sadroddin, Saeid Shahbaz, Farhad Mokhtari, Farahnaz Fadavinia

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The six sigma method is a project-driven management approach to improve the organization-s products, services, and processes by continually reducing defects in the organization. Understanding the key features, obstacles, and shortcomings of the six sigma method allows organizations to better support their strategic directions, and increasing needs for coaching, mentoring, and training. It also provides opportunities to better implement six sigma projects. The purpose of this paper is the survey of six sigma process and its impact on the organizational productivity. So I have studied key concepts , problem solving process of six sigmaas well as the survey of important fields such as: DMAIC, six sigma and productivity applied programme, and other advantages of six sigma. In the end of this paper, present research conclusions. (direct and positive relation between six sigma and productivity)

Keywords: Six sigma, project management, quality, theory, productivity.

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Authors: Krishnan V. Pagalthivarthi, Pankaj K. Gupta, Vipin Tyagi, M. R. Ravi

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Dense slurry flow through centrifugal pump casing has been modeled using the Eulerian-Eulerian approach with Eulerian multiphase model in FLUENT 6.1®. First order upwinding is considered for the discretization of momentum, k and ε terms. SIMPLE algorithm has been applied for dealing with pressurevelocity coupling. A mixture property based k-ε turbulence model has been used for modeling turbulence. Results are validated first against mesh independence and experiments for a particular set of operational and geometric conditions. Parametric analysis is then performed to determine the effect on important physical quantities viz. solid velocities, solid concentration and solid stresses near the wall with various operational geometric conditions of the pump.

Keywords: Centrifugal pump casing, Dense slurry, Solidsconcentration, Wall shear stress, Pump geometric parameters.

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Authors: M. Mordjaoui, B. Boudjema, M. Chabane, R. Daira

Abstract:

In determining the electromagnetic properties of magnetic materials, hysteresis modeling is of high importance. Many models are available to investigate those characteristics but they tend to be complex and difficult to implement. In this paper a new qualitative hysteresis model for ferromagnetic core is presented, based on the function approximation capabilities of adaptive neuro fuzzy inference system (ANFIS). The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach can restored the hysteresis curve with a little RMS error. The model accuracy is good and can be easily adapted to the requirements of the application by extending or reducing the network training set and thus the required amount of measurement data.

Keywords: ANFIS modeling technique, magnetic hysteresis, Jiles-Atherton model, ferromagnetic core.

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Authors: Brian A. Smith, Ronald W. McClendon, Gerrit Hoogenboom

Abstract:

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.

Keywords: Time-series forecasting, weather modeling.

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Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.

Keywords: Feature Selection, Learning with Kernels, Support Vector Machine, Genetic Algorithm, and Classification.

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

Abstract:

Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, three feature selection methods are evaluated: Random Selection, Information Gain (IG) and Support Vector Machine feature selection (called SVM_FS). We show that the best results were obtained with SVM_FS method for a relatively small dimension of the feature vector. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Feature Selection, Learning with Kernels, SupportVector Machine, and Classification.

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Authors: A. Taqaddas

Abstract:

Purpose: This E-survey was carried out to facilitate the implementation and Education of VMAT (Volumetric Modulated Arc Therapy) in Radiotherapy-RT departments and reasons for not using IMRT (Intensity Modulated Radiotherapy). VMAT Skills in demand were also identified. Method: E-Survey was distributed to NHS hospitals across UK by email. Thirty NHS and related centres in England, 21 in Scotland, 3 in Ireland and 1 in Wales were contacted. This Survey was intended for those working in RT and Medical Physics and who were responsible for Treatment Planning and training. Results: This E-survey have indicated pathways adopted by staff to acquire VMAT skills, strategies to efficiently implement VMAT in RT departments and for obtaining VMAT Education. Conclusion: Despite poor survey response this survey has managed to highlight requirements for education and implementation of VMAT that are also applicable to IMRT. Other RT centres in world can also find these results useful.

Keywords: IMRT, Radiotherapy, Treatment Planning, VMAT.

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Authors: H. El Fadili, K. Zenkouar, H. Qjidaa

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This paper proposes a neural network weights and topology optimization using genetic evolution and the backpropagation training algorithm. The proposed crossover and mutation operators aims to adapt the networks architectures and weights during the evolution process. Through a specific inheritance procedure, the weights are transmitted from the parents to their offsprings, which allows re-exploitation of the already trained networks and hence the acceleration of the global convergence of the algorithm. In the preprocessing phase, a new feature extraction method is proposed based on Legendre moments with the Maximum entropy principle MEP as a selection criterion. This allows a global search space reduction in the design of the networks. The proposed method has been applied and tested on the well known MNIST database of handwritten digits.

Keywords: Genetic algorithm, Legendre Moments, MEP, Neural Network.

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Authors: I. Oloyede

Abstract:

The study investigates the accuracy and loss of deep learning algorithms with the set of coronavirus (COVID-19) images dataset by comparing Bayesian convolutional neural network and traditional convolutional neural network in low dimensional dataset. 50 sets of X-ray images out of which 25 were COVID-19 and the remaining 20 were normal, twenty images were set as training while five were set as validation that were used to ascertained the accuracy of the model. The study found out that Bayesian convolution neural network outperformed conventional neural network at low dimensional dataset that could have exhibited under fitting. The study therefore recommended Bayesian Convolutional neural network (BCNN) for android apps in computer vision for image detection.

Keywords: BCNN, CNN, Images, COVID-19, Deep Learning.

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Authors: Sachin Kumar, Vasilis Sotiris, Michael Pecht

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With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysis. These methods have been used to detect deviations in system performance from normal operation and for critical parameter isolation in multivariate environments. The study evaluates the detection capability of each approach on a set of test data with known faults against a baseline set of data representative of such “healthy" systems.

Keywords: Mahalanobis distance, Principle components, Projection pursuit, Health assessment, Anomaly.

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Authors: J. Marzbanrad, A. Yadollahi

Abstract:

In the present paper, Fatigue life assessment of an anti-roll bar component of a passenger vehicle, is investigated by ANSYS 11 software. A stress analysis is also carried out by the finite element technique for the determination of highly stressed regions on the bar. Anti-roll bar is a suspension element used at the front, rear, or at both ends of a car that reduces body roll by resisting any unequal vertical motion between the pair of wheels to which it is connected. As a first stage, fatigue damage models proposed by some well-known references and the corresponding assumptions are discussed and some enhancements are proposed. Then, fracture analysis of an anti-roll bar of an automobile is carried out. The analysed type of the anti-roll bar is especially important as many cases are reported about the fracture after a 100,000 km of travel fatigue and fracture conditions. This paper demonstrates fatigue life of an anti-roll bar and then evaluated by experimental analytically results from other researcher.

Keywords: Anti-roll bar, Fracture, Fatigue life, Random loading

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Authors: Lina Yan, Jingjing Di, Ke Wang

Abstract:

A new basis function neural network algorithm is proposed for numerical integration. The main idea is to construct neural network model based on spline basis functions, which is used to approximate the integrand by training neural network weights. The convergence theorem of the neural network algorithm, the theorem for numerical integration and one corollary are presented and proved. The numerical examples, compared with other methods, show that the algorithm is effective and has the characteristics such as high precision and the integrand not required known. Thus, the algorithm presented in this paper can be widely applied in many engineering fields.

Keywords: Numerical integration, Spline basis function, Neural network algorithm

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Authors: El Youssfi L., Choukr-Allah R., Zaafrani M., Mediouni T., Ba Samba M., Hirich A

Abstract:

The purpose of this work was to study the effect of the irrigation using waste water with various electric conductivities (T(0,92ds/m), EC3 (3ds/m) and EC6 (6ds/m) on three varieties of quinoa cultivated in a field south of Morocco. The follow up of the evolution of the chemical and agronomic parameters throughout the culture made it possible to determine the responses to the saline stress in arid conditions. Results showed that the salinity caused the depression of plant-s height, and reduced the fresh and dry weight in the different parts of the three varieties plants. The increase of the irrigation water EC didn-t affect the yield for the varieties. Thus, quinoa resisted to salinity and proved a behavior of a facultative halophyte crop. In fact, the cultivation of this using treated wastewater is feasible especially in arid areas for a sustainable use of water resources.

Keywords: Quinoa, salinity, semi-arid, treated wastewater.

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Authors: Hidehiko Okada

Abstract:

The author previously proposed an extension of differential evolution. The proposed method extends the processes of DE to handle interval numbers as genotype values so that DE can be applied to interval-valued optimization problems. The interval DE can employ either of two interval models, the lower and upper model or the center and width model, for specifying genotype values. Ability of the interval DE in searching for solutions may depend on the model. In this paper, the author compares the two models to investigate which model contributes better for the interval DE to find better solutions. Application of the interval DE is evolutionary training of interval-valued neural networks. A result of preliminary study indicates that the CW model is better than the LU model: the interval DE with the CW model could evolve better neural networks. 

Keywords: Evolutionary algorithms, differential evolution, neural network, neuroevolution, interval arithmetic.

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Authors: H. Mashhady, B. Lotfi, M. Doosti, M. Fatollahi

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An exploration of the related literature reveals that all instruction methods aim at training autonomous learners. After the turn of second language pedagogy toward learner-oriented strategies, learners’ needs were more focused. Yet; the historical, social and political aspects of learning were still neglected. The present study investigates the notion of autonomous learning and explains its various facets from a pedagogical point of view. Furthermore; different elements, fields and scopes of autonomous learning will be explored. After exploring different aspects of autonomy, it is postulated that liberatory autonomy is highlighted since it not only covers social autonomy but also reveals learners’ capabilities and human potentials. It is also recommended that learners consider different elements of autonomy such as motivation, knowledge, confidence, and skills.

Keywords: Critical pedagogy, social autonomy, academic learning, cultural notions.

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Authors: Malinee Pongsavee

Abstract:

Sodium formate is the chemical substance used for food additive. Catalase is the important antioxidative enzyme in protecting the cell from oxidative damage by reactive oxygen species (ROS). The resultant level of oxidative stress in sodium formatetreated lymphocytes was investigated. The sodium formate concentrations of 0.05, 0.1, 0.2, 0.4 and 0.6 mg/mL were treated in human lymphocytes for 12 hours. After 12 treated hours, catalase activity change was measured in sodium formate-treated lymphocytes. The results showed that the sodium formate concentrations of 0.4 and 0.6 mg/mL significantly decreased catalase activities in lymphocytes (P < 0.05). The change of catalase activity in sodium formate-treated lymphocytes may be the oxidative damage marker for detect sodium formate exposure in human.

Keywords: Sodium formate, catalase activity, oxidative damage marker, toxicity.

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Authors: A. Ellen Kalinga, R. B. Burchard Bagile, Lena Trojer

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Information and Communications Technologies (ICT) has been integrated in education in many developing and developed countries alike, but the use of ICT in Tanzanian schools is dismal. Many Tanzanian secondary schools have no computers. The few schools with computers use them primarily for secretarial services and computer literacy training. The Tanzanian education system at other levels like secondary school level has to undergo substantial transformation, underscored by the growing application of new information and communication technology. This paper presents the e-readiness survey result from secondary schools in Tanzania. The paper also suggests how Tanzania can make use of the few present ICT resources to support and improve teaching and learning functions to improve performance and acquisition of knowledge by using e-Learning Management System (e-LMS).

Keywords: e-Learning, ICT, Object-Oriented, Participatorydesign.

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Authors: D. Khodadadi Dehkordi

Abstract:

This research was planned in order to study the effect of drought stress and different levels of Superabsorbent and their effect on grain yield, biologic yield and harvest index. In this study, 3 different depths of irrigation were considered as the main treatment I1, I2, I3 as 100, 75 and 50 percent of water requirement of plants respectively and different levels of Superabsorbent were used as secondary treatment (S0, S1, S2 and S3, equal to 0 (control), 15, 30 and 45 gr/m2 respectively). According to the results, independent effects of irrigation and Superabsorbent treatments at 1% level on biologic and grain yield of corn were significant. In addition, independent effect of irrigation treatments at 5% level on harvest index was significant. But independent effect of Superabsorbent treatments on harvest index was not significant.

Keywords: Corn, Deficit irrigation, Superabsorbent, Yield.

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Authors: Weidong Xia, Malgorzata Kolotylo, Xuan Tan

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This study examines the key factors that influence general practitioners’ learning and transfer of specialized arthritis knowledge and self-care techniques to patients during normal patient visits. Drawing on the theory of planed behavior and using matched survey data collected from general practitioners before and after training sessions provided by specialized orthopedic physicians, the study suggests that the general practitioner’s intention to use and transfer learned knowledge was influenced mainly by intrinsic motivation, organizational learning culture and absorptive capacity, but was not influenced by extrinsic motivation. The results provide both theoretical and practical implications.

Keywords: Empirical study, healthcare knowledge management, patient self-care, physician knowledge transfer.

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Authors: Jana Novakova, Lenka Petrkovska, Josef Brychta, Robert Cep, Lenka Ocenasova

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The contribution is dealing with the influence of high speed parameters on the quality of machined surface. In general the principle of high speed cutting lies in achieving faster machine times with concurrent increase in accuracy and quality of the machined areas in largely irregular, mathematically hard to define shapes. High speed machining is a highly effective method of machining with the following goals: increasing of machining productivity, increasing of quality of the machined surface, improving of machining economy, improving of ecological aspects of machining. This article is based on an experiment performed by the Department of Machining and Assembly of the Faculty of Mechanical Engineering of VŠBTechnical University of Ostrava.

Keywords: High speed cutting, measurement, surface integrity, surface roughness, residual stress/

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Authors: Fouzieh Rouzmehr, Mehdi Mousavi

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Hot mix asphalt concrete is a viscoelastic material, and its stress-strain relationship depends on the loading duration and the strain rate. To investigate the effect of elastic and viscoelastic modeling under traffic load, asphalt concrete pavement is modeled with both elastic and viscoelastic properties and the pavement performance is predicted. The differences of these two models are investigated on fatigue cracking and rutting problem which are the two main design parameters in flexible pavement design. Although the differences in rutting problem between two models were negligible, in fatigue cracking, the viscoelastic model results were more accurate. Results indicate that modeling the flexible pavement with elastic material is efficient enough and gives the acceptable results.

Keywords: Flexible pavement, asphalt, FEM modeling, viscoelastic, elastic, ANSYS.

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Authors: V. Andronie, M. Pârvu, V. Simion, I. Andronie

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The aim of this study was to trace the effects of growth environment on the behaviour of fattening pigs in the farm and outside it. Behavioral manifestations of pigs reared in pens with enriched environment (A lot, n: 22) were different from those of pigs reared in pens with arid environment (B lot, n: 17) in shelter and when the movement to be loaded. Pigs of B lot spent more time on the move (31%) compared to group A pigs (13%), and manifested more aggressive behavior when they were loaded. Salivary cortisol levels also showed high values for pigs in B lot after being removed from their growth environment, as compared to its concentration for A lot pigs. The enriched environment for pigs may determine different responses of behavior. Pigs raised in arid environment, were easier to loaded than pigs reared in enriched environment, but they responded to mixing and loading stress, through increases in cortisol concentrations and impaired behavioral manifestations.

Keywords: environment shelter, pig, behavior

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Authors: Kazunari Meguro, Shinichi Motomura, Takao Kawamura, Kazunori Sugahara

Abstract:

In this paper, backup and recovery technique for Peer to Peer applications, such as a distributed asynchronous Web-Based Training system that we have previously proposed. In order to improve the scalability and robustness of this system, all contents and function are realized on mobile agents. These agents are distributed to computers, and they can obtain using a Peer to Peer network that modified Content-Addressable Network. In the proposed system, although entire services do not become impossible even if some computers break down, the problem that contents disappear occurs with an agent-s disappearance. As a solution for this issue, backups of agents are distributed to computers. If a failure of a computer is detected, other computers will continue service using backups of the agents belonged to the computer.

Keywords: Distributed Multimedia Systems, e-Learning, P2P, Mobile Agent

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Authors: Sina Babadi, K. M. Ebrahimi

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The aim of this paper is to determine the stress levels at the end of a long slender shaft such as a drilling assembly used in the oil or gas industry using a mathematical model in real-time. The torsional deflection experienced by this type of drilling shaft (about 4 KM length and 20 cm diameter hollow shaft with a thickness of 1 cm) can only be determined using a distributed modeling technique. The main objective of this project is to calculate angular velocity and torque at the end of the shaft by TLM method and also analyzing of the behavior of the system by transient response. The obtained result is compared with lumped modeling technique the importance of these results will be evident only after the mentioned comparison. Two systems have different transient responses and in this project because of the length of the shaft transient response is very important.

Keywords: Distributed Lumped modeling, Lumped modeling, Drill string, Angular Velocity, Torque.

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Authors: Emilio A. Berny-Brandt, Sonia E. Ruiz

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Steel tubular towers serving as support structures for large wind turbines are subjected to several hundred million stress cycles caused by the turbulent nature of the wind. This causes highcycle fatigue, which could govern the design of the tower. Maintaining the support structure after the wind turbines reach its typical 20-year design life has become a common practice; however, quantifying the changes in the reliability on the tower is not usual. In this paper the effect of fatigue damage in the wind turbine structure is studied whit the use of fracture mechanics, and a method to estimate the reliability over time of the structure is proposed. A representative wind turbine located in Oaxaca, Mexico is then studied. It is found that the system reliability is significantly affected by the accumulation of fatigue damage. 

Keywords: Crack growth, fatigue, Monte Carlo simulation, structural reliability, wind turbines

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Authors: Maria E. Eggink

Abstract:

Innovation plays an important role in economic growth and development. Evolutionary economics has entrepreneurs at the centre of the innovation system, but includes all other participants as contributors to the performance of the innovation system. Education and training institutions, one of the participants in the innovation system, contributes in different ways to human capital. The gap in literature on the competence building as part of human capital in the analysis of innovation systems is addressed in this paper. The Mpumalanga Province of South Africa is used as a case study. It was found that the absence of a university, the level of education, the quality and performance in the education sector and the condition of the education infrastructure have not been conducive to learning.

Keywords: Education institutions, human capital, innovation systems, Mpumalanga Province.

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Authors: Z. Zainuddin, N. Mahat, Y. Abu Hassan

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Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.

Keywords: Backpropagation, Dynamic Adaptation Methods, Local Adaptive Techniques, Neural networks.

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Authors: Eric Jiang

Abstract:

As a popular rank-reduced vector space approach, Latent Semantic Indexing (LSI) has been used in information retrieval and other applications. In this paper, an LSI-based content vector model for text classification is presented, which constructs multiple augmented category LSI spaces and classifies text by their content. The model integrates the class discriminative information from the training data and is equipped with several pertinent feature selection and text classification algorithms. The proposed classifier has been applied to email classification and its experiments on a benchmark spam testing corpus (PU1) have shown that the approach represents a competitive alternative to other email classifiers based on the well-known SVM and naïve Bayes algorithms.

Keywords: Feature Selection, Latent Semantic Indexing, Text Classification, Vector Space Model.

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Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

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For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: High-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network.

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