Web-based network device management using SNMP servlet

As the complexity and size of data networks increases, network management faces a great challenge. Fortunately, the emergence of the SNMP (simple network management protocol) management framework and Web technology release network managers from the difficult circumstance. The paper explains the function and design of a prototype network device management station based on leading-edge Java servlet technologies and the World Wide Web. Web technologies bring valuable cost improvements, flexibility, and security enhancement to configuration management and performance management. The paper also discusses the functional requirements of Web-based network management and proposes the architecture for Web-based network configuration management, which is capable of controlling and monitoring remotely the configuration information of enterprise networks

An Adjoint Sensitivity Method Applied to Time Reverse Imaging of Tsunami Source for the 2009 Samoa Earthquake

We have previously developed a tsunami source inversion method based on “Time Reverse Imaging” and demonstrated that it is computationally very efficient and has the ability to reproduce the tsunami source model with good accuracy using tsunami data of the 2011 Tohoku earthquake tsunami. In this paper, we implemented this approach in the 2009 Samoa earthquake tsunami triggered by a doublet earthquake consisting of both normal and thrust faulting. Our result showed that the method is quite capable of recovering the source model associated with normal and thrust faulting. We found that the inversion result is highly sensitive to some stations that must be removed from the inversion. We applied an adjoint sensitivity method to find the optimal set of stations in order to estimate a realistic source model. We found that the inversion result is improved significantly once the optimal set of stations is used. In addition, from the reconstructed source model we estimated the slip distribution of the fault from which we successfully determined the dipping orientation of the fault plane for the normal fault earthquake. Our result suggests that the fault plane dip toward the northeast.This research has been financially supported by Japan Society for the Promotion of Science (JSPS) KAKENHI grant 16H01838 and Australian Research Council Discovery Project DP120103207

A hybrid-based modified adaptive fuzzy inference engine for pattern classification

The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the hybrid fuzzy clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed hybrid MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher's Iris data set and shown to be very competitive

Deploying natural language with topological relations for robotics behavior.

Topological descriptions and maps are key prerequisite to any autonomous machine for successful navigation in unpredictable behavior of environment. It is necessary to consider topological map with metric refines of 9-intersaction model for the movement of the robot and react its environment accurately. The aim of present study was to identify the robotics movement and its behavior using natural language spatial relations to process information for robot without computation failures and communication errors. The similarity measure of geometric interpretation is considered for computation for the movement of robot. The splitting metric concepts are used for robotics movement to determine the appropriate way to divide by line in a region. The 16 different metric parameters for natural-language spatial term are considered to find out the appropriate direction for the robotic movement. So that robot will react with behavior of environment with natural language spatial term. There are 32 natural language spatial terms are found for robotic movement from present study found and finally these natural languages with spatial relationship are deployed for robotics movement. All these natural language spatial terms could be used in any research related to spatial technology especially where autonomous machine is used

New perspectives on tsunami source inversion

Many tsunami source inversion techniques have already been developed to infer tsunami source models with the assumption that tsunami generation is due to slip on a single large fault. Therefore, these inversion techniques cannot determine to what extent subsidiary phenomena - such as submarine landslides, block movement, or slip on splay faults - have contributed to the tsunami generation. In addition to taking into account subsidiary phenomena, it is necessary to consider all the model complexities associated with tsunami generation and propagation including model physics, source kinematics and source discretization to infer a source model that can produce tsunami waveforms having a good agreement with observed waveforms. In my thesis, I have developed new methods using tsunami waveforms to estimate the properties of the source that depend as little as possible on assumptions about how it was generated. I first consider the importance of model parametrization, including dispersion, source kinematics and source discretization, in a conventional least-squares approach to tsunami source inversion. I implement single and multiple time window methods for dispersive and non-dispersive wave propagation to estimate source models for the tsunami generated by the 2011 Tohoku-oki earthquake. The results show that tsunami source models can strongly depend on such model choices, in particular for high quality data available today from ocean bottom pressure and global positioning system gauges. My results show that it is important to consider them together, rather than separately as has been done previously, in order to obtain more meaningful inversion results. I have also proposed a new method that can be used to derive source models without requiring the assumption of slip on a fault of pre-determined geometry or even knowing the earthquake source area. The proposed method is based on "Time Reverse Imaging (TRI)" technique, which has been used in underwater acoustic and medical imaging. We have applied TRI to recover the initial sea surface displacement associated with the tsunami source. To show the application of this method we have chosen the same tsunami event triggered by the March 11, 2011 Tohoku earthquake, for which an unprecedented number of high-quality observations are available. This method has been extended by combining TRI with conventional source inversion method. In conventional source inversion, the linear problem is solved by weighted least-squares method with regularization constraints to obtain stable and physically meaningful solutions. This approach solves the same linear problem but without using additional constraints or imposing weights of the stations. Instead, the parameters are determined by using time reverse imagining with the same Green's function from source to receiver and reversed observed waveforms. This method can overcome the uncertainties associated with observation weights and the choice of hyperparameters. The method has potential for use tsunami warning systems, as the method is computationally efficient and can be used to estimate initial source model by using precomputed Greens function in order to provide more accurate and realistic tsunami prediction

A framework of modified adaptive neuro-fuzzy inference engine

Neuro-fuzzy inference engine and/or system is knowledge based data processing system and can manage the human reasoning course and create decisions based on uncertainty and imprecise situations. Neuro-fuzzy systems are globally employed for pattern recognition, industrial plant control, system predictions, modeling and other decision making purposes. Neuro-fuzzy systems are very popular among researchers in various advanced promising fields to help solve problems with a small number of inputs (three or less). However, there are limitations faced by all popular neuro-fuzzy inference system architectures when they are applied to systems with a large number of inputs (more than three). One of the vital significant issues for constructing a high quality neuro-fuzzy system is the creation of the knowledge base, which mainly consists of membership functions and fuzzy rules. This thesis proposes a framework of modified adaptive neuro-fuzzy inference engine (MANFIE) for a diversity of practical applications in order to resolve the benchmark problems of a large number of inputs datasets. A modified apriori algorithm was employed to reduce the number of clusters effectively on the basis of common data in the clusters of every input to obtain a minimal set of decision rules based on datasets. The Takagi-Sugeno-Kang (TSK) type fuzzy inference system was chosen and constructed by an automatic generation of clusters as well as membership functions and minimal rules through the use of hybrid fuzzy clustering and the modified apriori algorithms respectively. The developed TSK type fuzzy inference engine is called modified adaptive fuzzy inference engine (MAFIE) and its parameters were then adjusted by the hybrid learning algorithm using adaptive neural network architecture towards improved performance which is called MANFIE. The performance of MANFIE was compared with existing methods in a diversity of practical benchmark applications such as pattern classifications, time series predictions, modeling with inverse learning control and mobile robot navigation. The MANFIE has shown the ability to reduce and form the robust minimal rules (Rules reduced on average 97.95% and 96.90% accuracy for pattern classifications, rules reduced on average 97.15%, 75% and 98.43% for time series predictions, modeling with inverse learning control and mobile robot navigation respectively) to make an appropriate structure and minimize the root mean square error (RMSE - 0.024, 0.149 for time series predictions, 0.007 for modeling with learning control, 0.027 for mobile robot navigation) with the best accuracy. The results of benchmark problems have shown improvement, competitiveness and satisfaction by showing a better system performance index with a less number of rules in each high input application. This study suggests that the MANFIE is a suitable modified framework as an adaptive neuro-fuzzy inference engine and is ready to be applied to practical application problems

An Approach to Recognize Handwritten Digits Using Machine Learning Classifiers

Handwritten digit recognition is one of the most important issues in the area of pattern recognition researches. There are many uses of handwritten digit recognition such as Bank check processing, sorting postal mail form, phone number data entry are common applications of automatic digit recognition. The sentiment of the problem deceits within the capability to develop an efficient algorithm that can recognize handwritten digits. Usually, these digits are normally found from scanning documents with digital devices. Typically storing handwritten digits such as phone number, bank account number, postal numbers and so is extremely troublesome with human intervention. An efficient handwritten digit recognition can eradicate this hazard. To eliminate the difficulties of recognizing handwritten digits, this paper proposes an approach using machine learning algorithms. The objective of this research is to present a reliable and effective approach to recognize handwritten digits. Several supervised machine learning classifiers were employed for the recognition and their accuracy are compared and discussed. The highest 97.07% accuracy is found by the Random Forest classifier

Naïve Bayes Based Multiple Parallel Fuzzy Reasoning Method For Medical Diagnosis

There are millions of sample medical cases recorded in many digital medical datasets that can be used by the data mining techniques for predicting any particular disease. Improving the classification accuracy in medical diagnosis based on patterns extracted from the available medical datasets is a challenging research problem as the medical datasets contain many complex patterns. In artificial intelligence, hybrid intelligent systems can support the data mining process to improve the accuracy of classification for medical diagnosis. Hybrid intelligent system is an integrated design of different artificial intelligence techniques such as neuro-fuzzy, genetic-fuzzy, etc., that has been successful in many applications such as data mining, computer vision, speech synthesis, etc. This paper proposes a hybrid intelligent method of integrating Naïve Bayes classifier and parallel fuzzy systems for the classification of type 2 diabetes. The proposed method employs multiple hybrid fuzzy systems in a parallel structure for effective classification on the data. The proposed method showed better classification accuracy of 90.26% when tested using the Pima diabetes dataset

A Robust Real-time Stress Detection System Using ECG and Neuro-Fuzzy Classification Method

A reliable solution for mutual sympathy in human computer interaction (HCI) has recently become a major issue in human life. • Machines disregard human emotion in the human computer interaction (no sympathy). • Stress in some social situations such as job interview can be tough even if one has gone through it many times. • Bio-signals are one of the most reliable input parameters for assessing human emotion. • The electrocardiogram signal (ECG) is one of the biosensors used in this study for stress detection

Blockchain integrated multi-agent system for breast cancer diagnosis

The integration of multi-agent system and blockchain technology can be beneficial to healthcare applications by providing intelligent data analysis with security. This paper presents an architecture that integrates multi-agent learning system and blockchain technology to support breast cancer diagnosis in a secured manner. The proposed system is based on a parallel hybrid fuzzy logic approach for supporting the prediction of breast cancer disease. The proposed system showed a classification accuracy of 96.49% in breast cancer diagnosis when testing with the Wisconsin Diagnostic Breast Cancer dataset. The blockchain is used to provide agent security in the proposed system to ensure that the only trusted and reputed agents are participated in the decision-making process