Search results for: Network security.

Authors: A. Lakrim, D. Tahri

Abstract:

This paper sets out a behavioral macro-model of a Merged PiN and Schottky (MPS) diode based on silicon carbide (SiC). This model holds good for both static and dynamic electrothermal simulations for industrial applications. Its parameters have been worked out from datasheets curves by drawing on the optimization method: Simulated Annealing (SA) for the SiC MPS diodes made available in the industry. The model also adopts the Analog Behavioral Model (ABM) of PSPICE in which it has been implemented. The thermal behavior of the devices was also taken into consideration by making use of Foster’ canonical network as figured out from electro-thermal measurement provided by the manufacturer of the device.

Keywords: SiC MPS Diode, electro-thermal, SPICE Model.

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Authors: K. Suganya Devi, P. Srinivasan, M. P. Vaishnave, G. Arutperumjothi

Abstract:

Today’s internet world is highly prone to various online attacks, of which the most harmful attack is phishing. The attackers host the fake websites which are very similar and look alike. We propose an image based authentication using steganography and visual cryptography to prevent phishing. This paper presents a secure steganographic technique for true color (RGB) images and uses Discrete Cosine Transform to compress the images. The proposed method hides the secret data inside the cover image. The use of visual cryptography is to preserve the privacy of an image by decomposing the original image into two shares. Original image can be identified only when both qualified shares are simultaneously available. Individual share does not reveal the identity of the original image. Thus, the existence of the secret message is hard to be detected by the RS steganalysis.

Keywords: Image security, random LSB, steganography, visual cryptography.

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Authors: A. Junaid, M. A. Z. Raja, I. M. Qureshi

Abstract:

An evolutionary computing technique for solving initial value problems in Ordinary Differential Equations is proposed in this paper. Neural network is used as a universal approximator while the adaptive parameters of neural networks are optimized by genetic algorithm. The solution is achieved on the continuous grid of time instead of discrete as in other numerical techniques. The comparison is carried out with classical numerical techniques and the solution is found with a uniform accuracy of MSE ≈ 10-9 .

Keywords: Neural networks, Unsupervised learning, Evolutionary computing, Numerical methods, Fitness evaluation function.

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Authors: Yong-Je Choi, Moo-Seop Kim, Hang-Rok Lee, Ho-Won Kim

Abstract:

Polynomial bases and normal bases are both used for elliptic curve cryptosystems, but field arithmetic operations such as multiplication, inversion and doubling for each basis are implemented by different methods. In general, it is said that normal bases, especially optimal normal bases (ONB) which are special cases on normal bases, are efficient for the implementation in hardware in comparison with polynomial bases. However there seems to be more examined by implementing and analyzing these systems under similar condition. In this paper, we designed field arithmetic operators for each basis over GF(2233), which field has a polynomial basis recommended by SEC2 and a type-II ONB both, and analyzed these implementation results. And, in addition, we predicted the efficiency of two elliptic curve cryptosystems using these field arithmetic operators.

Keywords: Elliptic Curve Cryptosystem, Crypto Algorithm, Polynomial Basis, Optimal Normal Basis, Security.

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Authors: Thang Manh Hoang

Abstract:

Recent research result has shown that two multidelay feedback systems can synchronize each other under different schemes, i.e. lag, projective-lag, anticipating, or projectiveanticipating synchronization. There, the driving signal is significantly complex due that it is constituted by multiple nonlinear transformations of delayed state variable. In this paper, a secure communication model is proposed based on synchronization of coupled multidelay feedback systems, in which the plain signal is mixed with a complex signal at the transmitter side and it is precisely retrieved at the receiver side. The effectiveness of the proposed model is demonstrated and verified in the specific example, where the message signal is masked directly by the complex signal and security is examined under the breaking method of power spectrum analysis.

Keywords: chaos synchronization, time-delayed system, chaosbasedsecure communications

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Authors: Dale Dzemydiene, Aurelija Burinskiene, Arunas Miliauskas, Kristina Ciziuniene

Abstract:

Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Keywords: Multi-objective decision support, analysis, data validation, freight delivery, multi-modal transportation, genetic programming methods.

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Authors: Mehran Yazdi, Kazem Gheysari

Abstract:

In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for classifying fingerprints into four common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.

Keywords: Biometrics, fingerprint classification, gray level cooccurrence matrix, regular texture representation.

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Authors: Nour Charara, Hussein Charara, Omar Abou Khaled, Hani Abdallah, Elena Mugellini

Abstract:

In this paper, we present a human behavior modeling approach in videos scenes. This approach is used to model the normal behaviors in the conference halls. We exploited the Probabilistic Latent Semantic Analysis technique (PLSA), using the 'Bag-of-Terms' paradigm, as a tool for exploring video data to learn the model by grouping similar activities. Our term vocabulary consists of 3D spatio-temporal patch groups assigned by the direction of motion. Our video representation ensures the spatial information, the object trajectory, and the motion. The main importance of this approach is that it can be adapted to detect abnormal behaviors in order to ensure and enhance human security.

Keywords: Activity modeling, clustering, PLSA, video representation.

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Authors: Xiaoou Li, Wen Yu

Abstract:

Crude oil blending is an important unit operation in petroleum refining industry. A good model for the blending system is beneficial for supervision operation, prediction of the export petroleum quality and realizing model-based optimal control. Since the blending cannot follow the ideal mixing rule in practice, we propose a static neural network to approximate the blending properties. By the dead-zone approach, we propose a new robust learning algorithm and give theoretical analysis. Real data of crude oil blending is applied to illustrate the neuro modeling approach.

Keywords: Neural networks, modeling, stability, crude oil.

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Authors: Satyendra Nath Mandal, J. Pal Choudhury, Dilip De, S. R. Bhadra Chaudhuri

Abstract:

The production of a plant can be measured in terms of seeds. The generation of seeds plays a critical role in our social and daily life. The fruit production which generates seeds, depends on the various parameters of the plant, such as shoot length, leaf number, root length, root number, etc When the plant is growing, some leaves may be lost and some new leaves may appear. It is very difficult to use the number of leaves of the tree to calculate the growth of the plant.. It is also cumbersome to measure the number of roots and length of growth of root in several time instances continuously after certain initial period of time, because roots grow deeper and deeper under ground in course of time. On the contrary, the shoot length of the tree grows in course of time which can be measured in different time instances. So the growth of the plant can be measured using the data of shoot length which are measured at different time instances after plantation. The environmental parameters like temperature, rain fall, humidity and pollution are also play some role in production of yield. The soil, crop and distance management are taken care to produce maximum amount of yields of plant. The data of the growth of shoot length of some mustard plant at the initial stage (7,14,21 & 28 days after plantation) is available from the statistical survey by a group of scientists under the supervision of Prof. Dilip De. In this paper, initial shoot length of Ken( one type of mustard plant) has been used as an initial data. The statistical models, the methods of fuzzy logic and neural network have been tested on this mustard plant and based on error analysis (calculation of average error) that model with minimum error has been selected and can be used for the assessment of shoot length at maturity. Finally, all these methods have been tested with other type of mustard plants and the particular soft computing model with the minimum error of all types has been selected for calculating the predicted data of growth of shoot length. The shoot length at the stage of maturity of all types of mustard plants has been calculated using the statistical method on the predicted data of shoot length.

Keywords: Fuzzy time series, neural network, forecasting error, average error.

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Authors: Yanyan Xu, Lizhi Xiong, Zhengquan Xu, Li Jiang

Abstract:

In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to the cloud. However, this causes difficulties in image retrieval and data management. A secure image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that the user can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms. Experimental results prove that the proposed method can achieve better security and better retrieval precision.

Keywords: Secure image retrieval, secure search, orthogonal decomposition, secure cloud computing.

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Authors: Jong-won Lee

Abstract:

Since Cloud environment has appeared as the most powerful keyword in the computing industry, the growth in VDI (Virtual Desktop Infrastructure) became remarkable in domestic market. In recent years, with the trend that mobile devices such as smartphones and pads spread so rapidly, the strengths of VDI that allows people to access and perform business on the move along with companies' office needs expedite more rapid spread of VDI. In this paper, mobile OTP (One-Time Password) authentication method is proposed to secure mobile device portability through rapid and secure authentication using mobile devices such as mobile phones or pads, which does not require additional purchase or possession of OTP tokens of users. To facilitate diverse and wide use of Services in the future, service should be continuous and stable, and above all, security should be considered the most important to meet advanced portability and user accessibility, the strengths of VDI.

Keywords: Cloud, VDI, OTP, Mobility

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Authors: Sheena Mathew, K. Poulose Jacob

Abstract:

This paper describes the results of an extensive study and comparison of popular hash functions SHA-1, SHA-256, RIPEMD-160 and RIPEMD-320 with JERIM-320, a 320-bit hash function. The compression functions of hash functions like SHA-1 and SHA-256 are designed using serial successive iteration whereas those like RIPEMD-160 and RIPEMD-320 are designed using two parallel lines of message processing. JERIM-320 uses four parallel lines of message processing resulting in higher level of security than other hash functions at comparable speed and memory requirement. The performance evaluation of these methods has been done by using practical implementation and also by using step computation methods. JERIM-320 proves to be secure and ensures the integrity of messages at a higher degree. The focus of this work is to establish JERIM-320 as an alternative of the present day hash functions for the fast growing internet applications.

Keywords: Cryptography, Hash function, JERIM-320, Messageintegrity

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Authors: Radwa Mabrook

Abstract:

Virtual Reality (VR) content creation is a complex and an expensive process, which requires multi-disciplinary teams of content creators. Grant schemes from technology companies help media organisations to explore the VR potential in journalism and factual storytelling. Media organisations try to do as much as they can in-house, but they may outsource due to time constraints and skill availability. Journalists, game developers, sound designers and creative artists work together and bring in new cultures of work. This study explores the collaborative experimental nature of VR content creation, through tracing every actor involved in the process and examining their perceptions of the VR work. The study builds on Actor Network Theory (ANT), which decomposes phenomena into their basic elements and traces the interrelations among them. Therefore, the researcher conducted 22 semi-structured interviews with VR content creators between November 2017 and April 2018. Purposive and snowball sampling techniques allowed the researcher to recruit fact-based VR content creators from production studios and media organisations, as well as freelancers. Interviews lasted up to three hours, and they were a mix of Skype calls and in-person interviews. Participants consented for their interviews to be recorded, and for their names to be revealed in the study. The researcher coded interviews’ transcripts in Nvivo software, looking for key themes that correspond with the research questions. The study revealed that VR content creators must be adaptive to change, open to learn and comfortable with mistakes. The VR content creation process is very iterative because VR has no established work flow or visual grammar. Multi-disciplinary VR team members often speak different languages making it hard to communicate. However, adaptive content creators perceive VR work as a fun experience and an opportunity to learn. The traditional sense of competition and the strive for information exclusivity are now replaced by a strong drive for knowledge sharing. VR content creators are open to share their methods of work and their experiences. They target to build a collaborative network that aims to harness VR technology for journalism and factual storytelling. Indeed, VR is instilling collaborative and experimental cultures in journalism.

Keywords: Collaborative culture, content creation, experimental culture, virtual reality.

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Authors: Dan Ohev Zion, Alon Cohen

Abstract:

In recent years, the presence of short-range millimeter-wave radar in civil application has increased significantly. Autonomous driving, security, 3D imaging and high data rate communication systems are a few examples. The next challenge is the integration inside small form-factor devices, such as smartphones (e.g. gesture recognition). The main challenge is implementation of a truly low-power, low-complexity high-resolution radar. The most popular approach is the Frequency Modulated Continuous Wave (FMCW) radar, with an analog multiplication front-end. In this paper, we present an approach for adaptive estimation and correction of impairments of such front-end, specifically implemented using the Six-Port Device (SPD) as the multiplier element. The proposed algorithm was simulated and implemented on a 60 GHz radar lab prototype.

Keywords: Radar, millimeter-wave, six-port, FMCW Radar, IQ mismatch.

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Authors: D. Wrzecionkowska

Abstract:

Mass media campaigns against obesity are often designed to impact large audiences. This usually means that their audience is defined based on general demographic characteristics like age, gender, occupation etc., not taking into account psychographics like behavior, motivations, wants, etc. Using psychographics, as the base for the audience segmentation, is a common practice in case of successful campaigns, as it allows developing more relevant messages. It also serves a purpose of identifying key segments, those that generate the best return on investment. For a health campaign, that would be segments that have the best chance of being converted into healthy lifestyle at the lowest cost. This paper presents the limitations of the demographic targeting, based on the findings from the reception study of IMSS (Mexican Social Security Institute) antiobesity TV commercials and proposes mothers as the first level of segmentation, in the process of identifying the key segment for these campaigns.

Keywords: Anti-obesity campaigns, mothers, segmentation, targeting.

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Authors: Le Thanh Ha, Hye-Soo Kim, Chun-Su Park, Seung-Won Jung, Sung-Jea Ko

Abstract:

Flexible macroblock ordering (FMO), adopted in the H.264 standard, allows to partition all macroblocks (MBs) in a frame into separate groups of MBs called Slice Groups (SGs). FMO can not only support error-resilience, but also control the size of video packets for different network types. However, it is well-known that the number of bits required for encoding the frame is increased by adopting FMO. In this paper, we propose a novel algorithm that can reduce the bitrate overhead caused by utilizing FMO. In the proposed algorithm, all MBs are grouped in SGs based on the similarity of the transform coefficients. Experimental results show that our algorithm can reduce the bitrate as compared with conventional FMO.

Keywords: Data Partition, Entropy Coding, Greedy Algorithm, H.264/AVC, Slice Group.

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Authors: Ashwaq Alsulami, Jianhua Shao

Abstract:

Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.

Keywords: Attributed community, attribute detection, community, social network.

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Authors: József Valyon, Gábor Horváth

Abstract:

Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they eliminate certain crucial questions involved by neural network construction. The main drawback of standard SVM is its high computational complexity, therefore recently a new technique, the Least Squares SVM (LS–SVM) has been introduced. In this paper we present an extended view of the Least Squares Support Vector Regression (LS–SVR), which enables us to develop new formulations and algorithms to this regression technique. Based on manipulating the linear equation set -which embodies all information about the regression in the learning process- some new methods are introduced to simplify the formulations, speed up the calculations and/or provide better results.

Keywords: Function estimation, Least–Squares Support VectorMachines, Regression, System Modeling

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Authors: Nugzar Todua

Abstract:

The paper studies the role of food labeling in order to promote healthy eating issue in Georgia. The main focus of the research is directed to consumer attitudes regarding food labeling. The methodology of the paper is based on the focus group work, as well as online and face to face surveys. The data analysis has been provided through ANOVA. The study proves that the impact of variables such as the interest, awareness, reliability, assurance and satisfaction of consumers' on buying decision, is statistically important. The study reveals that consumers’ perception regarding to food labeling is positive, but their level of knowledge and ability is rather low. It is urgent to strengthen marketing promotions strategies in the process of implementations of food security policy in Georgia.

Keywords: Food labeling, buying decision, Georgian consumers, marketing research.

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Authors: Eshagh Rasouli Sarabi, Mir Saeed Moosavi

Abstract:

Road transportation system is the most important method of transporting the goods. Considering the most suitable geographical situation of Iran to transport the goods between Europe and Asia and placement of this country in direction of international corridors (east- west) , (north-south) and Asian land transport to infrastructure development “A.L.T.I.D" and Transport corridor Europe - Caucasus - Asia “T.R.A.C.E.C.A", noticing the security of road transportation system in this country is so important. In this paper the main factors of accidents on the roads of Iran are categorized regarding the rate of accidents occurred. Then apart from studying the main reasons of accidents of every category, the main factors of these events are studied and its strategies in Iran are introduced.

Keywords: Road, Transportation, Accident, Factors

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Authors: Mohammad Ali Baradaran Ghahfarokhi, Parvin Baradaran Ghahfarokhi

Abstract:

In this paper, first we introduce the stable distribution, stable process and theirs characteristics. The a -stable distribution family has received great interest in the last decade due to its success in modeling data, which are too impulsive to be accommodated by the Gaussian distribution. In the second part, we propose major applications of alpha stable distribution in telecommunication, computer science such as network delays and signal processing and financial markets. At the end, we focus on using stable distribution to estimate measure of risk in stock markets and show simulated data with statistical softwares.

Keywords: stable distribution, SaS, infinite variance, heavy tail networks, VaR.

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Authors: Hemant Kumar, Nilendu Das

Abstract:

A study based on ANN structure gives us the information to predict the size of the future in realizing a past event. ANN, IMD (Indian meteorological department) data and remote sensing were used to enable a number of parameters for calculating the size that may occur in the future. A threshold selected specifically above the high-frequency harvest reached the area during the selected seismic activity. In the field of human and local biodiversity it remains to obtain the right parameter compared to the frequency of impact. But during the study the assumption is that predicting seismic activity is a difficult process, not because of the parameters involved here, which can be analyzed and funded in research activity.

Keywords: ANN, Bayesian class, earthquakes, IMD.

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Authors: Shian Wan, Yi-Chang Wang, Yu-Chien Lin

Abstract:

This research discussed the various theories of service design, the importance of service design methodology, and the development of transformative service design framework. In this study, transformative service design is applied while building a new brand community service for women, children and infants retailing business. The goal is to enhance the brand recognition and customer loyalty, effectively increase the brand community engagement by embedding the brand community in social network and ultimately, strengthen the impact and the value of the company brand.

Keywords: Service design, transformative service design, brand community.

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Authors: Justin Leo Cheang Loong, Khazaimatol S Subari, Rosli Besar, Muhammad Kamil Abdullah

Abstract:

In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.

Keywords: biometric, ecg, linear predictive coding, wavelet packet decomposition

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Authors: Dong-Woo Lim, Chang-Jae Chun, Hyung-Myung Kim

Abstract:

We consider a two-way relay network where two sources exchange information. A relay helps the two sources exchange information using the decode-and-XOR-forward protocol. We investigate the power minimization problem with minimum rate constraints. The system needs two time slots and in each time slot the required rate pair should be achievable. The power consumption is minimized in each time slot and we obtained the closed form solution. The simulation results confirm that the proposed power allocation scheme consumes lower total power than the conventional schemes.

Keywords: Decode-and-XOR-forward, power minimization, two-way relay

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Authors: Bharat B. Gulyani, Anuj Jain, Shalendra Kumar

Abstract:

Customarily, the LMTD correction factor, FT, is used to screen alternative designs for a heat exchanger. Designs with unacceptably low FT values are discarded. In this paper, authors have proposed a more fundamental criterion, based on feasibility of a multipass exchanger as the only criteria, followed by economic optimization. This criterion, coupled with asymptotic energy targets, provide the complete optimization space in a heat exchanger network (HEN), where cost-optimization of HEN can be performed with only Heat Recovery Approach temperature (HRAT) and number-of-shells as variables.

Keywords: heat exchanger, heat exchanger networks, LMTD correction factor, shell targeting.

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Authors: H. El Ayech, A. Trabelsi

Abstract:

In this article we are going to discuss the improvement of the multi classes- classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes- subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two classes- models, the elected class will be the strongest one that won-t lose any competition with the other classes. Rates of recognition gotten with the multi class-s approach by two-class-s decomposition are clearly better that those gotten by the simple multi class-s approach.

Keywords: Artificial neural network, letter-recognition, Multi class Classification, Multi Layer Perceptron.

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Authors: Birol Yildiz, Abdullah Yalama, Metin Coskun

Abstract:

Many studies have shown that Artificial Neural Networks (ANN) have been widely used for forecasting financial markets, because of many financial and economic variables are nonlinear, and an ANN can model flexible linear or non-linear relationship among variables. The purpose of the study was to employ an ANN models to predict the direction of the Istanbul Stock Exchange National 100 Indices (ISE National-100). As a result of this study, the model forecast the direction of the ISE National-100 to an accuracy of 74, 51%.

Keywords: Artificial Neural Networks, Istanbul StockExchange, Non-linear Modeling.

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Authors: Lili Wang, Meng Hu

Abstract:

In this paper, a class of recurrent neural networks (RNNs) with variable delays are studied on almost periodic time scales, some sufficient conditions are established for the existence and global exponential stability of the almost periodic solution. These results have important leading significance in designs and applications of RNNs. Finally, two examples and numerical simulations are presented to illustrate the feasibility and effectiveness of the results.

Keywords: Recurrent neural network, Almost periodic solution, Global exponential stability, Time scale.

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