Search results for: real time data.

Authors: Aneta Oblouková, Eva Vítková

Abstract:

The article deals with the issue of water and sewerage charge rate prices in the Czech Republic. The research is specifically focused on the analysis of the development of the average prices of water and sewerage charge rate in the Czech Republic in 1994-2021 and on the validation of the chosen methodology relevant for the prediction of the development of the average prices of water and sewerage charge rate in the Czech Republic. The research is based on data collection. The data for this research were obtained from the Czech Statistical Office. The aim of the paper is to validate the relevance of the mathematical linear trend estimate technique for the calculation of the predicted average prices of water and sewerage charge rates. The real values of the average prices of water and sewerage charge rates in the Czech Republic in 1994-2018 were obtained from the Czech Statistical Office and were converted into a mathematical equation. The same type of real data was obtained from the Czech Statistical Office for 2019-2021. Prediction of the average prices of water and sewerage charge rates in the Czech Republic in 2019-2021 was also calculated using a chosen method – a linear trend estimation technique. The values obtained from the Czech Statistical Office and the values calculated using the chosen methodology were subsequently compared. The research result is a validation of the chosen mathematical technique to be a suitable technique for this research.

Keywords: Czech Republic, linear trend estimation, price prediction, water and sewerage charge rate.

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Authors: Bo Liu, Qingshan Kong, Weiqing Huang

Abstract:

With the rapid development of modern communication, diagnosing the fiber-optic quality and faults in real-time is widely focused. In this paper, a Labview-based system is proposed for fiber-optic faults detection. The wavelet threshold denoising method combined with Empirical Mode Decomposition (EMD) is applied to denoise the optical time domain reflectometer (OTDR) signal. Then the method based on Gabor representation is used to detect events. Experimental measurements show that signal to noise ratio (SNR) of the OTDR signal is improved by 1.34dB on average, compared with using the wavelet threshold denosing method. The proposed system has a high score in event detection capability and accuracy. The maximum detectable fiber length of the proposed Labview-based system can be 65km.

Keywords: Empirical mode decomposition (EMD), events detection, Gabor transform, optical time domain reflectometer (OTDR), wavelet threshold denoising.

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Authors: Rupali, Anil Kumar Jaiswal

Abstract:

Cloud computing can be defined as one of the prominent technologies that lets a user change, configure and access the services online. it can be said that this is a prototype of computing that helps in saving cost and time of a user practically the use of cloud computing can be found in various fields like education, health, banking etc.  Cloud computing is an internet dependent technology thus it is the major responsibility of Cloud Service Providers(CSPs) to care of data stored by user at data centers. Scheduling in cloud computing environment plays a vital role as to achieve maximum utilization and user satisfaction cloud providers need to schedule resources effectively.  Job scheduling for cloud computing is analyzed in the following work. To complete, recreate the task calculation, and conveyed scheduling methods CloudSim3.0.3 is utilized. This research work discusses the job scheduling for circulated processing condition also by exploring on this issue we find it works with minimum time and less cost. In this work two load balancing techniques have been employed: ‘Throttled stack adjustment policy’ and ‘Active VM load balancing policy’ with two brokerage services ‘Advanced Response Time’ and ‘Reconfigure Dynamically’ to evaluate the VM_Cost, DC_Cost, Response Time, and Data Processing Time. The proposed techniques are compared with Round Robin scheduling policy.

Keywords: Physical machines, virtual machines, support for repetition, self-healing, highly scalable programming model.

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Authors: Hyun-Woo Cho

Abstract:

Detection of incipient abnormal events is important to improve safety and reliability of machine operations and reduce losses caused by failures. Improper set-ups or aligning of parts often leads to severe problems in many machines. The construction of prediction models for predicting faulty conditions is quite essential in making decisions on when to perform machine maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of machine measurement data. The calibration model is used to predict two faulty conditions from historical reference data. This approach utilizes genetic algorithms (GA) based variable selection, and we evaluate the predictive performance of several prediction methods using real data. The results shows that the calibration model based on supervised probabilistic principal component analysis (SPPCA) yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: Prediction, operation monitoring, on-line data, nonlinear statistical methods, empirical model.

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Authors: Rida B. Arieby, Hameed N. Hameed

Abstract:

In this work, tensile tests on high density polyethylene have been carried out under various constant strain rate and strain rate jump tests. The dependency of the true stress and specially the variation of volume strain have been investigated, the volume strain due to the phenomena of damage was determined in real time during the tests by an optical extensometer called Videotraction. A modified constitutive equations, including strain rate and damage effects, are proposed, such a model is based on a non-equilibrium thermodynamic approach called (DNLR). The ability of the model to predict the complex nonlinear response of this polymer is examined by comparing the model simulation with the available experimental data, which demonstrate that this model can represent the deformation behavior of the polymer reasonably well.

Keywords: Strain rate jump tests, Volume Strain, High Density Polyethylene, Large strain, Thermodynamics approach.

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Authors: S. Bourennane, A. Bendjama

Abstract:

This paper deals with the localization of the wideband sources. We develop a new approach for estimating the wide band sources parameters. This method is based on the high order statistics of the recorded data in order to eliminate the Gaussian components from the signals received on the various hydrophones.In fact the noise of sea bottom is regarded as being Gaussian. Thanks to the coherent signal subspace algorithm based on the cumulant matrix of the received data instead of the cross-spectral matrix the wideband correlated sources are perfectly located in the very noisy environment. We demonstrate the performance of the proposed algorithm on the real data recorded during an underwater acoustics experiments.

Keywords: Higher-order statistics, high resolution array processing techniques, localization of acoustics sources, wide band sources.

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Authors: Chia-Hau Liu, Tai-Yue Wang

Abstract:

Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Keywords: Multivariate control chart, statistical process control, one-class classification method.

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Authors: Muhammad Marsudi, Hani Shafeek

Abstract:

The purpose of this paper is to simulate the production process of a metal stamping industry and to evaluate the utilization of the production line by using ARENA simulation software. The process time and the standard time for each process of the production line is obtained from data given by the company management. Other data are collected through direct observation of the line. There are three work stations performing ten different types of processes in order to produce a single product type. Arena simulation model is then developed based on the collected data. Verification and validation are done to the Arena model, and finally the result of Arena simulation can be analyzed. It is found that utilization at each workstation will increase if batch size is increased although throughput rate remains/is kept constant. This study is very useful for the company because the company needs to improve the efficiency and utilization of its production lines.

Keywords: Arena software, case study, production line, utilization.

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Authors: C. Kalamani, K. Paramasivam

Abstract:

In VLSI, testing plays an important role. Major problem in testing are test data volume and test power. The important solution to reduce test data volume and test time is test data compression. The Proposed technique combines the bit maskdictionary and 2n pattern run length-coding method and provides a substantial improvement in the compression efficiency without introducing any additional decompression penalty. This method has been implemented using Mat lab and HDL Language to reduce test data volume and memory requirements. This method is applied on various benchmark test sets and compared the results with other existing methods. The proposed technique can achieve a compression ratio up to 86%.

Keywords: Bit Mask dictionary, 2n pattern run length code, system-on-chip, SOC, test data compression.

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

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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: Mohamad Mahdavi

Abstract:

Most of the real queuing systems include special properties and constraints, which can not be analyzed directly by using the results of solved classical queuing models. Lack of Markov chains features, unexponential patterns and service constraints, are the mentioned conditions. This paper represents an applied general algorithm for analysis and optimizing the queuing systems. The algorithm stages are described through a real case study. It is consisted of an almost completed non-Markov system with limited number of customers and capacities as well as lots of common exception of real queuing networks. Simulation is used for optimizing this system. So introduced stages over the following article include primary modeling, determining queuing system kinds, index defining, statistical analysis and goodness of fit test, validation of model and optimizing methods of system with simulation.

Keywords: Estimation, queuing system, simulation model, probability distribution, non-Markov chain.

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Authors: M. A. Torkamani, S. Mahmoodzadeh, S. Pourroostaei, C. Lucas

Abstract:

Daily production of information and importance of the sequence of produced data in forecasting future performance of market causes analysis of data behavior to become a problem of analyzing time series. But time series that are very complicated, usually are random and as a result their changes considered being unpredictable. While these series might be products of a deterministic dynamical and nonlinear process (chaotic) and as a result be predictable. Point of Chaotic theory view, complicated systems have only chaotically face and as a result they seem to be unregulated and random, but it is possible that they abide by a specified math formula. In this article, with regard to test of strange attractor and biggest Lyapunov exponent probability of chaos on several foreign exchange rates vs. IRR (Iranian Rial) has been investigated. Results show that data in this market have complex chaotic behavior with big degree of freedom.

Keywords: Chaos, Exchange Rate, Nonlinear Models.

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Authors: Rahma Souli-Jbali, Minyar Sassi Hidri, Rahma Ben Ayed

Abstract:

Grid of computing nodes has emerged as a representative means of connecting distributed computers or resources scattered all over the world for the purpose of computing and distributed storage. Since fault tolerance becomes complex due to the availability of resources in decentralized grid environment, it can be used in connection with replication in data grids. The objective of our work is to present fault tolerance in data grids with data replication-driven model based on clustering. The performance of the protocol is evaluated with Omnet++ simulator. The computational results show the efficiency of our protocol in terms of recovery time and the number of process in rollbacks.

Keywords: Data grids, fault tolerance, chandy-lamport, clustering.

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Authors: Ahmad Habibizad Navin, Mehdi Naghian Fesharaki, Mohamad Teshnelab, Ehsan Shahamatnia

Abstract:

The anti-lock braking systems installed on vehicles for safe and effective braking, are high-order nonlinear and timevariant. Using fuzzy logic controllers increase efficiency of such systems, but impose a high computational complexity as well. The main concept introduced by this paper is reducing computational complexity of fuzzy controllers by deploying problem-solution data structure. Unlike conventional methods that are based on calculations, this approach is based on data oriented modeling.

Keywords: ABS, Fuzzy controller, PSDS, Time-Memory tradeoff, Data oriented modeling.

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Authors: Rami N. Khushaba, Adel Al-Jumaily

Abstract:

The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

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Authors: Tanja Radu, Cormac Fay, King Tong Lau, Rhys Waite, Dermot Diamond

Abstract:

The development of wearable sensing technologies is a great challenge which is being addressed by the Proetex FP6 project (www.proetex.org). Its main aim is the development of wearable sensors to improve the safety and efficiency of emergency personnel. This will be achieved by continuous, real-time monitoring of vital signs, posture, activity, and external hazards surrounding emergency workers. We report here the development of carbon dioxide (CO2) sensing boot by incorporating commercially available CO2 sensor with a wireless platform into the boot assembly. Carefully selected commercially available sensors have been tested. Some of the key characteristics of the selected sensors are high selectivity and sensitivity, robustness and the power demand. This paper discusses some of the results of CO2 sensor tests and sensor integration with wireless data transmission

Keywords: Proetex, gas sensing, wireless, wearable sensors, carbon dioxide

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Authors: Ashly Joseph, Jithu Paulose

Abstract:

Large language models (LLMs) have revolutionized natural language processing capabilities, enabling applications such as chatbots, dialogue agents, image, and video generators. Nevertheless, their trainings on extensive datasets comprising personal information poses notable privacy and safety hazards. This study examines methods for addressing these challenges, specifically focusing on approaches to enhance the security of LLM outputs, safeguard user privacy, and adhere to data protection rules. We explore several methods including post-processing detection algorithms, content filtering, reinforcement learning from human and AI inputs, and the difficulties in maintaining a balance between model safety and performance. The study also emphasizes the dangers of unintentional data leakage, privacy issues related to user prompts, and the possibility of data breaches. We highlight the significance of corporate data governance rules and optimal methods for engaging with chatbots. In addition, we analyze the development of data protection frameworks, evaluate the adherence of LLMs to General Data Protection Regulation (GDPR), and examine privacy legislation in academic and business policies. We demonstrate the difficulties and remedies involved in preserving data privacy and security in the age of sophisticated artificial intelligence by employing case studies and real-life instances. This article seeks to educate stakeholders on practical strategies for improving the security and privacy of LLMs, while also assuring their responsible and ethical implementation.

Keywords: Data privacy, large language models, artificial intelligence, machine learning, cybersecurity, general data protection regulation, data safety.

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Authors: A. Y. Bani Hashim, N. S. A. Ramdan

Abstract:

Inconsistency in manual inspection is real because humans get tired after some time. Recent trends show that automatic inspection is more appealing for mass production inspections. In such as a case, a robot manipulator seems the best candidate to run a dynamic visual inspection. The purpose of this work is to estimate the optimum workspace where a robot manipulator would perform a visual inspection process onto a work piece where a camera is attached to the end effector. The pseudo codes for the planned path are derived from the number of tool transit points, the delay time at the transit points, the process cycle time, and the configuration space that the distance between the tool and the work piece. It is observed that express start and swift end are acceptable in a robot program because applicable works usually in existence during these moments. However, during the mid-range cycle, there are always practical tasks programmed to be executed. For that reason, it is acceptable to program the robot such as that speedy alteration of actuator displacement is avoided. A dynamic visual inspection system using a robot manipulator seems practical for a work piece with a complex shape.

Keywords: Robot manipulator, Visual inspection, Work piece, Trajectory planning.

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Authors: Mona A. Fouad, Fatma M. Bayoumi, Hoda M. Onsi, Mohamed G. Darwish

Abstract:

Digital libraries become more and more necessary in order to support users with powerful and easy-to-use tools for searching, browsing and retrieving media information. The starting point for these tasks is the segmentation of video content into shots. To segment MPEG video streams into shots, a fully automatic procedure to detect both abrupt and gradual transitions (dissolve and fade-groups) with minimal decoding in real time is developed in this study. Each was explored through two phases: macro-block type's analysis in B-frames, and on-demand intensity information analysis. The experimental results show remarkable performance in detecting gradual transitions of some kinds of input data and comparable results of the rest of the examined video streams. Almost all abrupt transitions could be detected with very few false positive alarms.

Keywords: Adaptive threshold, abrupt transitions, gradual transitions, MPEG video streams.

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Authors: K.Sedhuraman, S.Himavathi, A.Muthuramalingam

Abstract:

The performance of sensor-less controlled induction motor drive depends on the accuracy of the estimated speed. Conventional estimation techniques being mathematically complex require more execution time resulting in poor dynamic response. The nonlinear mapping capability and powerful learning algorithms of neural network provides a promising alternative for on-line speed estimation. The on-line speed estimator requires the NN model to be accurate, simpler in design, structurally compact and computationally less complex to ensure faster execution and effective control in real time implementation. This in turn to a large extent depends on the type of Neural Architecture. This paper investigates three types of neural architectures for on-line speed estimation and their performance is compared in terms of accuracy, structural compactness, computational complexity and execution time. The suitable neural architecture for on-line speed estimation is identified and the promising results obtained are presented.

Keywords: Sensorless IM drives, rotor speed estimators, artificial neural network, feed- forward architecture, single neuron cascaded architecture.

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Authors: S. Moein, S. A. Monadjemi, P. Moallem

Abstract:

In this paper, application of artificial neural networks in typical disease diagnosis has been investigated. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Then after selecting some symptoms of eight different diseases, a data set contains the information of a few hundreds cases was configured and applied to a MLP neural network. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. Outcomes suggest the role of effective symptoms selection and the advantages of data fuzzificaton on a neural networks-based automatic medical diagnosis system.

Keywords: Artificial Neural Networks, Fuzzy Logic, MedicalDiagnosis, Symptoms, Fuzzification.

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Authors: Amon Tunwannarux, Supanunt Tunwannarux

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This paper presents design features of a rescue robot, named CEO Mission II. Its body is designed to be the track wheel type with double front flippers for climbing over the collapse and the rough terrain. With 125 cm. long, 5-joint mechanical arm installed on the robot body, it is deployed not only for surveillance from the top view but also easier and faster access to the victims to get their vital signs. Two cameras and sensors for searching vital signs are set up at the tip of the multi-joint mechanical arm. The third camera is at the back of the robot for driving control. Hardware and software of the system, which controls and monitors the rescue robot, are explained. The control system is used for controlling the robot locomotion, the 5-joint mechanical arm, and for turning on/off devices. The monitoring system gathers all information from 7 distance sensors, IR temperature sensors, 3 CCD cameras, voice sensor, robot wheels encoders, yawn/pitch/roll angle sensors, laser range finder and 8 spare A/D inputs. All sensors and controlling data are communicated with a remote control station via IEEE 802.11b Wi-Fi. The audio and video data are compressed and sent via another IEEE 802.11g Wi-Fi transmitter for getting real-time response. At remote control station site, the robot locomotion and the mechanical arm are controlled by joystick. Moreover, the user-friendly GUI control program is developed based on the clicking and dragging method to easily control the movement of the arm. Robot traveling map is plotted from computing the information of wheel encoders and the yawn/pitch data. 2D Obstacle map is plotted from data of the laser range finder. The concept and design of this robot can be adapted to suit many other applications. As the Best Technique awardee from Thailand Rescue Robot Championship 2006, all testing results are satisfied.

Keywords: Controlling, monitoring, rescue robot, mechanicalarm.

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Authors: Qihe Li, Yupin Luo

Abstract:

Camera calibration plays an important role in the domain of the analysis of sports video. Considering soccer video, in most cases, the cross-points can be used for calibration at the center of the soccer field are not sufficient, so this paper introduces a new automatic camera calibration algorithm focus on solving this problem by using the properties of images of the center circle, halfway line and a touch line. After the theoretical analysis, a practicable automatic algorithm is proposed. Very little information used though, results of experiments with both synthetic data and real data show that the algorithm is applicable.

Keywords: Absolute conic, camera calibration, circular points, line at infinity.

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Authors: E. Dahlen

Abstract:

Inflation can be calculated from either the prices of consumer products or from salaries. This paper presents a logical exercise that shows it is easier to calculate inflation from salaries than from consumer products. While the prices of consumer products may change due to technological advancement, such as automation, which must be corrected for, salaries do not. If technological advancements are not accounted for within calculations based on consumer product prices, inflation can be confused with real wage changes, since both inflation and real wage changes affect the prices of consumer products. The method employed in this paper is a logical exercise. Logical arguments are presented that suggest the existence of many different feasible ways by which inflation can be determined. Then a short mathematical exercise will be presented which shows that one of these methods –using salaries – contains the fewest number of unknown parameters, and hence, is the preferred method, since the risk of mistakes is lower. From the results, it can be concluded that salaries, rather than consumer products, should be used to calculate inflation.

Keywords: Inflation, logic, math, real wages.

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Authors: Ajay Sharma, R.S. Jangid

Abstract:

Analytical seismic response of multi-story building supported on base isolation system is investigated under real earthquake motion. The superstructure is idealized as a shear type flexible building with lateral degree-of-freedom at each floor. The force-deformation behaviour of the isolation system is modelled by the bi-linear behaviour which can be effectively used to model all isolation systems in practice. The governing equations of motion of the isolated structural system are derived. The response of the system is obtained numerically by step-by-method under three real recorded earthquake motions and pulse motions associated in the near-fault earthquake motion. The variation of the top floor acceleration, interstory drift, base shear and bearing displacement of the isolated building is studied under different initial stiffness of the bi-linear isolation system. It was observed that the high initial stiffness of the isolation system excites higher modes in base-isolated structure and generate floor accelerations and story drift. Such behaviour of the base-isolated building especially supported on sliding type of isolation systems can be detrimental to sensitive equipment installed in the building. On the other hand, the bearing displacement and base shear found to reduce marginally with the increase of the initial stiffness of the initial stiffness of the isolation system. Further, the above behaviour of the base-isolated building was observed for different parameters of the bearing (i.e. post-yield stiffness and characteristic strength) and earthquake motions (i.e. real time history as well as pulse type motion).

Keywords: base isolation, base shear, bi-linear, earthquake, floor accelerations, inter-story drift, multi-story building, pulsemotion, stiffness ratio.

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R²) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran.

Keywords: Time series modelling, ARIMA model, River runoff, Karkheh River, CLS method.

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Authors: V. Venkatachalam, S. Selvan

Abstract:

The security of computer networks plays a strategic role in modern computer systems. Intrusion Detection Systems (IDS) act as the 'second line of defense' placed inside a protected network, looking for known or potential threats in network traffic and/or audit data recorded by hosts. We developed an Intrusion Detection System using LAMSTAR neural network to learn patterns of normal and intrusive activities, to classify observed system activities and compared the performance of LAMSTAR IDS with other classification techniques using 5 classes of KDDCup99 data. LAMSAR IDS gives better performance at the cost of high Computational complexity, Training time and Testing time, when compared to other classification techniques (Binary Tree classifier, RBF classifier, Gaussian Mixture classifier). we further reduced the Computational Complexity of LAMSTAR IDS by reducing the dimension of the data using principal component analysis which in turn reduces the training and testing time with almost the same performance.

Keywords: Binary Tree Classifier, Gaussian Mixture, IntrusionDetection System, LAMSTAR, Radial Basis Function.

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Authors: I. Arockiarani

Abstract:

The focus of the paper is to furnish the entropy measure for a neutrosophic set and neutrosophic soft set which is a measure of uncertainty and it permeates discourse and system. Various characterization of entropy measures are derived. Further we exemplify this concept by applying entropy in various real time decision making problems.

Keywords: Entropy measure, Hausdorff distance, neutrosophic set, soft set.

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Authors: R. Behmanesh, I. Rahimi

Abstract:

recurrent neural network (RNN) is an efficient tool for modeling production control process as well as modeling services. In this paper one RNN was combined with regression model and were employed in order to be checked whether the obtained data by the model in comparison with actual data, are valid for variable process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of models. First, the regression was made for predicting the response time of process based upon determined factors, and then the error between actual and predicted response time as output and also the same factors as input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, in training process of RNN, design of experiments was set so as to optimize the RNN.

Keywords: RNN, DOE, regression, control chart.

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Authors: Dr. L.K. Singh, Dr. R. M. L, Riktesh Srivastava

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How to efficiently assign system resource to route the Client demand by Gateway servers is a tricky predicament. In this paper, we tender an enhanced proposal for autonomous recital of Gateway servers under highly vibrant traffic loads. We devise a methodology to calculate Queue Length and Waiting Time utilizing Gateway Server information to reduce response time variance in presence of bursty traffic. The most widespread contemplation is performance, because Gateway Servers must offer cost-effective and high-availability services in the elongated period, thus they have to be scaled to meet the expected load. Performance measurements can be the base for performance modeling and prediction. With the help of performance models, the performance metrics (like buffer estimation, waiting time) can be determined at the development process. This paper describes the possible queue models those can be applied in the estimation of queue length to estimate the final value of the memory size. Both simulation and experimental studies using synthesized workloads and analysis of real-world Gateway Servers demonstrate the effectiveness of the proposed system.

Keywords: Gateway Server, G/M/1 Queuing Model.

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