International Journal of Intelligent Systems and Applications @ijisa
Journal articles - International Journal of Intelligent Systems and Applications
All articles: 1264
Scientific article
Cyberbullying is an intentional action of harassment along the complex domain of social media utilizing information technology online. This research experimented unsupervised associative approach on text mining technique to automatically find cyberbullying words, patterns and extract association rules from a collection of tweets based on the domain / frequent words. Furthermore, this research identifies the relationship between cyberbullying keywords with other cyberbullying words, thus generating knowledge discovery of different cyberbullying word patterns from unstructured tweets. The study revealed that the type of dominant frequent cyberbullying words are intelligence, personality, and insulting words that describe the behavior, appearance of the female victims and sex related words that humiliate female victims. The results of the study suggest that we can utilize unsupervised associative approached in text mining to extract important information from unstructured text. Further, applying association rules can be helpful in recognizing the relationship and meaning between keywords with other words, therefore generating knowledge discovery of different datasets from unstructured text.
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Scientific article
In this paper, an efficient control algorithm for an Intelligent Controller Induction Motor Drive system using Fuzzy Logic Approach has been proposed. The Indirect Vector Control principle has been employed to control the Induction Motor. Next, a two-degree-of freedom controller is proposed to improve the system performance. The controller design algorithm can be applied in an adjustable speed control system to obtain good transient responses and good load disturbance rejection abilities. The proposed controller has been analyzed using computer simulation and compared with a simple conventional Controller strategy. The simulated controller performances have been finally verified experimentally using TMS320C6711 Digital Signal Processor. The results obtained substantiate the robustness and effectiveness of Intelligent Controller for high performance of Induction Motor.
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Analysis towards Mobile IPV4 and Mobile IPV6 in Computer Networks
Scientific article
With the rapid growth in the number of mobile devices like cellular phones, personal digital assistants (PDAs), and laptop computers, the demand for “anywhere, anytime, and any way” high-speed Internet access is becoming a primary concern in our lives. Mobile IP has been designed with the Internet Engineering Task Force (IETF) to serve the needs of growing population of mobile computer users who wish to connect to the internet and maintain communication as they move from place to place. Mobile IP enables a wireless network node to move freely from one point of connection to the Internet to another, without disrupting the end-to-end connectivity. The goals of this paper are to define the fundamentals of mobile IPV4, elaborate the problems of mobile IPV4, give a brief overview of some of the literature that deals with the fundamentals of mobile IPV6, explain the problems of mobile IPV6, compare Mobile IPv6 and Mobile IPv4, discuss the advantages of mobile IP, and review the application of mobile IP for vehicular networks.
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Analytical Assessment of Security Level of Distributed and Scalable Computer Systems
Scientific article
The article deals with the issues of the security of distributed and scalable computer systems based on the risk-based approach. The main existing methods for predicting the consequences of the dangerous actions of the intrusion agents are described. There is shown a generalized structural scheme of job manager in the context of a risk-based approach. Suggested analytical assessments for the security risk level in the distributed computer systems allow performing the critical time values forecast for the situation analysis and decision-making for the current configuration of a distributed computer system. These assessments are based on the number of used nodes and data links channels, the number of active security and monitoring mechanisms at the current period, as well as on the intensity of the security threats realization and on the activation intensity of the intrusion prevention mechanisms. The proposed comprehensive analytical risks assessments allow analyzing the dynamics of intrusions processes, the dynamics of the security level recovery and the corresponding dynamics of the risks level in the distributed computer system.
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Ant Colony Optimization for Train Scheduling: An Analysis
Scientific article
This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO) technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS) and Standard Train Scheduling (STS) algorithm has been performed.
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Ant colony system algorithm with dynamic pheromone updating for 0/1 knapsack problem
Scientific article
The 0/1 Knapsack (KP) is a combinatorial optimization problem that can be solved using various optimization algorithms. Ant Colony System (ACS) is one of these algorithms that is operated iteratively and converged emphatically to a matured solution. The convergence of the ACS depends mainly on the heuristic patterns that are used to update the pheromone trails throughout the optimization cycles. Although, ACS has significant advantages, it suffers from a slow convergence, as the pheromones, which are used to initiate the searching process are initialized randomly at the beginning. In this paper, a new heuristic pattern is proposed to speed up the convergence of ACS with 0/1 KP. The proposed heuristic enforces an order-critical item selection. As such, the proposed heuristic depends on considering the profit added by each item, as similar to the existing heuristics, besides the order of item selection. Accordingly, the proposed heuristic allows the items that are added at the end to get more value in order to be considered in the beginning of the next round. As such, with each cycle, the selected items are varied substantially and the pheromones are vastly updated in order to avoid long trapping with the initial values that are initialized randomly. The experiments showed that the proposed heuristic is converged more rapidly compared to the existing heuristics by reducing up to 30% of the cycles required to reach the optimal solution using difficult 0/1 KP datasets. Accordingly, the times required for convergence have been reduced significantly in the proposed work compared to the time required by the existing algorithms.
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Anti-Spam Software for Detecting Information Attacks
Scientific article
In this paper the development of anti-spam software detecting information attacks is offered. For this purpose it is considered spam filtration system with the multilayered, multivalent architecture, coordinating all ISP’s in the country. All users and ISPs of this system involved in spam filtration process. After spam filtering process, saved spam templates are analyzed and classified. This parameterizing of spam templates give possibility to define the thematic dependence from geographical. For example, what subjects prevail in spam messages sent from the certain countries? Analyzing origins of spam templates from spam-base, it is possible to define and solve the organized social networks of spammers. Thus, the offered system will be capable to reveal purposeful information attacks if those occur.
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Application Research of Ontology-enabled Process FMEA Knowledge Management Method
Scientific article
Failure Mode and Effect Analysis (FMEA) is an important method to ensure the effectiveness of manufacturing process, which can prevent the happening of various potential failure. If the knowledge of FMEA can be accumulated and utilized reasonably, the quality management in manufacturing process will get decision support timely, and the efficiency of product quality problem analysis also can be improved. But, it is impossible to integrate the dispersive FMEA knowledge in the manufacturing process because there is a lack of unified management criterion of FMEA knowledge. In order to satisfy the requirement to share, reuse, and maintain FMEA knowledge, the representation method of FMEA knowledge based on ontology is put forward in this study. The structure of FMEA knowledge ontology can be described via visual modeling tool- Unified Modeling Language. In this study, the FMEA repository has been built and the acquisition, storage and searching of FMEA knowledge has been researched to satisfy the need of sharing and reusing of the FMEA knowledge in the manufacturing process.
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Application Research on High Resolution Radar Target Aggregation
Scientific article
In high resolution radar system, the same target always has original data; so we need to merge multiple data from the same target as one target. Because of the system’s real-time requirement, we usually have to carry out target aggregation as quickly as possible. In this paper, we propose a quick target aggregation method based on clustering algorithm. The proposed method divides original data into subsets by single dimensional distance, and then merges subsets according to single dimensional distance and setdensity. At last we apply the proposed method to carry out target aggregation for airport scene surveillance radar system. Experimental result shows the proposed method has high execution efficiency and is not sensitive to noise data; it is useful for high resolution radar target aggregation.
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Application of Adaptive Neural Network Observer in Chaotic Systems
Scientific article
Chaos control is an important subject in control theory. Chaos control usually confronts with some problems due to unavailability of states or losing the system characteristics during the modeling process. In this situation, using an appropriate observer in control strategy may overcome the problem. In this paper, states are estimated using an observer without having complete prior information from nonlinear term based on neural network. Simulation results verify performance of the proposed structure in estimating nonlinear term specifically for an online practical use.
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Application of Data Mining in the Classification of Historical Monument Places
Scientific article
The economic development and promotion of a country or region is depends on several facts such as- tourism, industries, transport, technology, GDP etc. The Government of the country is responsible to facilitate the opportunities to develop tourism, technology, transport etc. In view of this, we look into the Department of Tourism to predict and classify the number of tourists visiting historical Indian monuments such as Taj- Mahal, Agra, and Ajanta etc.. The data set is obtained from the Indian Tourist Statistics which contains year wise statistics of visitors to historical monuments places. A survey undertaken every year by the government is preprocessed to fill out the possible missing values, and normalize inconsistent data. Various classification techniques under Decision Tree approach such as- Random Tree, REPTree, Random Forest and J48 algorithms are applied to classify the historical monuments places. Performance evaluation measures of the classification models are analyzed and compared as a step in the process of knowledge discovery.
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Scientific article
This paper considers the impact of the Inductive Fault Current Limiter (IFCL) on directional overcurrent relays coordination. The coordination problem is formulated as a non-linear constrained mono-objective optimization problem. The objective function of this optimization problem is the minimization of the operation time of the associated relays in the systems, and the decision variables are: the time dial setting (TDS) and the pickup current setting (IP) of each relay. To solve this complex non linear optimization problem, a variant of optimization algorithms named Firefly Algorithm (FA) is used. The proposed method is tested on 8-bus power transmission systems test systems considering the influences of current relay under three-phases short-circuit with and without IFCL for different locality. The results show the effectiveness of the solution.
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Scientific article
This paper presents a non traditional optimization technique, genetic algorithm to seek the optimal allocation, type and size of FACTS devices to control line flows, to maintain bus voltage to desired level and to minimize system losses. The targeted objectives are maximizing the static security margins and voltage stability while minimizing losses. Congestion management is also done by optimally placing FACTS controllers with line outage. Matlab coding has been developed for the purpose of simulation. Assessments are done on IEEE 30 bus system against different loading conditions with two FACTS devices SVC and TCSC implemented in steady state and the results verify the potency of propound algorithm to find the optimal location for power system stability.
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Application of Generalized Measure of 'Useful' R-norm Inaccuracy and Total Ambiguity
Scientific article
In the present paper, we introduce generalized measure of 'useful' R-norm inaccuracy having two parameters and its analogue 'useful' R-norm total ambiguity measure by merging together the concepts of probability, fuzziness, R-norm, 'useful' information and inaccuracy. Along with the basic properties, some other important properties of these two proposed measures are stated. These measures are generalizations of some well-known inaccuracy measures. Further, the monotonic behaviour of the proposed 'useful' R-norm inaccuracy measures is studied and the graphical overview is given. The measure of information improvement for both the measures is also obtained. Lastly, the application of 'useful' R-norm total ambiguity measure is presented in terms of multi-criteria decision making. For all the numerical calculations R software is used.
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Application of Genetic Neural Network in Power Battery Charging State-of-Charge Estimation
Scientific article
With global non-renewable resources and environmental issues becoming more apparent, the development of new energy vehicles have become the trend of auto industry. Hybrid vehicle becomes the key development of new energy vehicles with its long distance, low pollution, low fuel consumption characteristics and so on. The battery performances directly influence the quality of the whole vehicle performance. Considering the importance of the battery state of charge (SOC) estimation and the nonlinear relationship between the battery SOC and the external characteristic, genetic algorithm (GA) and back propagation (BP) neural network are proposed. Because of the strong global search capability of the genetic algorithm and the generalization ability of BP neural network, the hybrid vehicle Ni-MH power battery GA-BP charging model is designed. In this approach, the network training speed is superior to the traditional BP network. According to the real-time data of the batteries, the optimal solution can be concluded in a short time and with high estimation precision.
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Application of Intelligent Agents in Wireless Prepaid Energy Meter
Scientific article
Prepaid meter (PM) is getting very popular especially in developing countries. There are many advantages to use prepaid meter as opposed to postpaid meter both to the utility provider and to the consumer. Brunei has adopted PM but it is not intelligent and not wireless enabled. Reading meters and topping up balance are still done manually. Utility provider does not have information on the usage statistics and has only limited functionalities in the grid control. So accordingly an intelligent agent based wireless prepaid energy meter been developed using JADE-LEAP Agent development kit allowing agent from utility provider to query wireless energy meter for energy values for every household. These statistics can be used for statistical computation of the power consumed and for policy and future planning. Agent from consumers' mobile devices can query the energy meter to study the power consumed and for topping up the balance. When the meter reaches the threshold, agent at energy meter would also send messages to alert consumers for topping up through mobile handset and failing to do so will lead to power being cut automatically
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Application of Intensified Current Search to Multiobjective PID Controller Optimization
Scientific article
The intelligent control system design has been changed from the conventional approach to the optimization framework solved by efficient metaheuristics. The intensified current search (ICS) has been recently proposed as one of the most powerful metaheuristics for solving optimization problems. The ICS, the latest modified version of the conventional current search (CS), possesses the memory list (ML) regarded as the exploration strategy and the adaptive radius (AR) and adaptive neighborhood (AN) mechanisms regarded as the exploitation strategy. The ML is used to escape from local entrapment caused by any local solution, while both AR and AN mechanisms are conducted to speed up the search process. In this paper, the application of the ICS to multiobjective PID controller design optimization for the three-phase induction motor (3Φ-IM) speed control system is proposed. Algorithms of the ICS and its performance evaluation against multiobjective functions are presented. As simulation results, the ICS can provide very satisfactory solutions for all test functions and the 3Φ;-IM control application. Moreover, the simulation results of motor control application are confirmed by the experimental results based on dSPACE technology.
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Scientific article
The desire to achieve an adaptive prognostics regression learning processes of physical and empirical phenomenon is a complex task and open problem in radio frequency telecommunication engineering. One key method to solving such complex task or problems is by means of numerical based optimisation algorithms. The Levenberg–Marquardt algorithm (LMA) is an efficient nonlinear parametric machine learning based modelling algorithm with optimal, fast, and accurate convergence speed. This paper proposes and demonstrates the real-time application of the LMA in developing a log-distance like propagation loss model based on received radio strength measurements conducted over deployed long term evolution (LTE) eNodeBs antennas in three different propagation areas. The LTE eNodeB signal propagation areas were selected to reflect typical urban, suburban and rural terrains which represent urban, suburban and rural terrains. The heights of the three eNodeBs are 30, 28 and 32m respectively and each operate at 2.6GHz carrier frequency with 10MHz channel bandwidths. The resultant outcome of the proposed propagation loss modelling using LMA indicates a high approximation efficacy over the popular Gauss-Newton algorithm (GNA) modelling method, which has been used to benchmark the process. Precisely, the developed propagation loss model using LMA method attained lower maximum absolute error (MABE) of 7.73, 14.57and 10.53 for urban, suburban and rural terrains compared to the ones developed by GNA which yielded 15.19, 16.59 and 13.05 MABE values. The improved approximation performance of the LMA over the GNA can be ascribed to its capacity handle multiple free parameters and attain optimum solution irrespective of the selected values of initial guess parameters.
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Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem
Scientific article
It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO) algorithm which is based on Ant Colony System (ACS) with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO) shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.
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Application of Neural Network on Burr Expert System in Micro-machining
Scientific article
The demands placed by designers on workpiece performance and functionality are increasing rapidly. Important aspects of manufacturing’s contribution to the fulfillment of these demands are the conditions at the work piece edges. However, Burrs are often created on the workpiece edges in micro-machining. In many cases, time consuming and expensive deburring processes have to be applied in order to ensure the desired part functionality. Burrs make troubles on production lines in terms of deburring cost, quality of products and cutting tool wear. To prevent problems caused by burrs in micro-machining, prediction and control of burr size is desirable. Experimental studies show that burr formation in micro-milling is a highly complex process depending on a number of parameters such as material properties, tool geometry and cutting parameters. It is very difficult to establish the relationship between burr sizes and cutting conditions. A web-based micro-machining burr expert system for burr sizes prediction and control was developed using ASP.NET platform. Burrs types and sizes prediction and cutting conditions optimization for burr controlling which based on the reasoning method of BP neural networks are realized. Operation results show that the system is reliable. It provides a new technology for burrs modelling and controlling.
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