Showing papers in "International Journal of Intelligent Systems and Applications in 2015"

Heart Diseases Diagnosis Using Neural Networks Arbitration

Abstract: There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.

A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals

Abstract: In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto- regressive model. Two algorithms are implemented for auto- regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.

GPS based Advanced Vehicle Tracking and Vehicle Control System

Abstract: Security systems and navigators have always been a necessity of human"s life. The developments of advanced electronics have brought revolutionary changes in these fields. In this paper, we will present a vehicle tracking system that employs a GPS module and a GSM modem to find the location of a vehicle and offers a range of control features. To complete the design successfully, a GPS unit, two relays, a GSM Modem and two MCU units are used. There are five features introduced in the project. The aim of this project is to remotely track a vehicle"s location, remotely switch ON and OFF the vehicle"s ignition system and remotely lock and unlock the doors of the vehicle. An SMS message is sent to the tracking system and the system responds to the users request by performing appropriate actions. Short text messages are assigned to each of these features. A webpage is specifically designed to view the vehicle"s location on Google maps. By using relay based control concept introduced in this paper, number of control features such as turning heater on/off, radio on/off etc. can be implemented in the same fashion. to the user with the location coordinates (i.e. Longitude and Latitude). These coordinates can be used to view the location of a vehicle on Google maps. The vehicle tracking system presented in this paper comprises of a cost effective and special tracking technology. It offers an advanced tracking and a variety of control features that facilitate the monitoring and clever control of the vehicle. The tracking systems are not only bounded to shipping industry and fleet tracking but also used in cars as a theft prevention tool. This paper provides an overview of the background research related to vehicle tracking and control systems, component"s choice and full development process of the tracking system. The paper is divided in five main sections: related research, choice of components, design of a system, simulation of designs and implementation process. In the related research section, we will outline the research carried out so far. Then, we will discuss the components used. The design section will focus the software and hardware design process. The assembly of components will be discussed in the implementation section. Finally, the implementation process section will include the software simulations and images of the hardware in working condition.

Evaluation of Feature Selection Approaches for Urdu Text Categorization

Abstract: Efficient feature selection is an important phase of designing an effective text categorization system. Various feature selection methods have been proposed for selecting dissimilar feature sets. It is often essential to evaluate that which method is more effective for a given task and what size of feature set is an effective model selection choice. Aim of this paper is to answer these questions for designing Urdu text categorization system. Five widely used feature selection methods were examined using six well-known classification algorithms: naive Bays (NB), k-nearest neighbor (KNN), support vector machines (SVM) with linear, polynomial and radial basis kernels and decision tree (i.e. J48). The study was conducted over two test collections: EMILLE collection and a naive collection. We have observed that three feature selection methods i.e. information gain, Chi statistics, and symmetrical uncertain, have performed uniformly in most of the cases if not all. Moreover, we have found that no single feature selection method is best for all classifiers. While gain ratio out-performed others for naive Bays and J48, information gain has shown top performance for KNN and SVM with polynomial and radial basis kernels. Overall, linear SVM with any of feature selection methods including information gain, Chi statistics or symmetric uncertain methods is turned-out to be first choice across other combinations of classifiers and feature selection methods on moderate size naive collection. On the other hand, naive Bays with any of feature selection method have shown its advantage for a small sized EMILLE corpus.

An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

Abstract: A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties and solves prediction, filtering and smoothing tasks of non-stationary "noisy" stochastic and chaotic signals. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

Evolution of Knowledge Representation and Retrieval Techniques

Abstract: Existing knowledge systems incorporate knowledge retrieval techniques that represent knowledge as rules, facts or a hierarchical classification of objects. Knowledge representation techniques govern validity and precision of knowledge retrieved. There is a vital need to bring intelligence as part of knowledge retrieval techniques to improve existing knowledge systems. Researchers have been putting tremendous efforts to develop knowledge-based system that can support functionalities of the human brain. The intention of this paper is to provide a reference for further research into the field of knowledge representation to provide improved techniques for knowledge retrieval. This review paper attempts to provide a broad overview of early knowledge representation and retrieval techniques along with discussion on prime challenges and issues faced by those systems. Also, state-of-the-art technique is discussed to gather advantages and the constraints leading to further research work. Finally, an emerging knowledge system that deals with constraints of existing knowledge systems and incorporates intelligence at nodes, as well as links, is proposed.

Energy Aware Ad Hoc On-Demand Multipath Distance Vector Routing

Abstract: The current disjoint path Ad hoc On-Demand Multi- path Distance Vector (AOMDV) routing protocol does not have any energy-awareness guarantees. When AOMDV is used in wireless sensor networks (WSNs) energy is an important consideration. To enhance the AOMDV protocol an extra energy metric is added along with the hop count metric. This Energy aware or EA-AOMDV improves path selection using a trade-off between energy and hop count, thus giving more longevity to WSNs. EA-AOMDV is compared to the current AOMDV routing protocol to prove its worth in the context of WSNs. It is found that EA-AOMDV leads to better WSN energy-awareness in resource constrained WSNs.

Arabic Text Categorization Using Logistic Regression

Abstract: Several Text Categorization (TC) techniques and algorithms have been investigated in the limited research literature of Arabic TC. In this research, Logistic Regression (LR) is investigated in Arabic TC. To the best of our knowledge, LR was never used for Arabic TC before. Experiments are conducted on Aljazeera Arabic News (Alj-News) dataset. Arabic text-preprocessing takes place on this dataset to handle the special nature of Arabic text. Experimental results of this research prove that the LR classifier is a competitive Arabic TC algorithm to the state of the art ones in this field; it has recorded a precision of 96.5% on one category and above 90% for 3 categories out of the five categories of Alj-News dataset. Regarding the overall performance, LR has recorded a macroaverage precision of 87%, recall of 86.33% and F- measure of 86.5%.

Data Mining of Students' Performance : Turkish Students as a Case Study

Abstract: Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task. The performances obtained from these networks were evaluated in consideration of achieved recognition rates and training time.

Deep Learning in Character Recognition Considering Pattern Invariance Constraints

Abstract: Character recognition is a field of machine learning that has been under research for several decades. The particular success of neural networks in pattern recognition and therefore character recognition is laudable. Research has also long shown that a single hidden layer network has the capability to approximate any function; while, the problems associated with training deep networks therefore led to little attention given to it. Recently, the breakthrough in training deep networks through various pre-training schemes have led to the resurgence and massive interest in them, significantly outperforming shallow networks in several pattern recognition contests; moreover the more elaborate distributed representation of knowledge present in the different hidden layers concords with findings on the biological visual cortex. This research work reviews some of the most successful pre-training approaches to initializing deep networks such as stacked auto encoders, and deep belief networks based on achieved error rates. More importantly, this research also parallels investigating the performance of deep networks on some common problems associated with pattern recognition systems such as translational invariance, rotational invariance, scale mismatch, and noise. To achieve this, Yoruba vowel characters databases have been used in this research.

Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Abstract: 3 Abstract— Parkinson is a neurological disease and occurs due to lack of dopamine neurons These dopamine neurons manage all body movements Parkinson patients have difficulty in doing all daily routine activities, and also have disturbed vocal fold movements Using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way In this paper, we have used 23 features dataset, all the features are analyzed and 15 features are selected from the total dataset As in Parkinson tremor is present in the voice box muscles, so the variation in the period and amplitude of consecutive vocal cycles is present The feature dataset selected consist of jitter, shimmer, harmonic to noise ratio, DFA, spread1 and PPE Various classifiers are used and their comparison is done to find out which classifier is perfect in this environment It is concluded that support vector classifiers as the best one with an accuracy of 96% In the neural network classifiers with different transfer functions, there is tradeoff among the performance parameters

Multi-objective Structural Optimization Using Fuzzy and Intuitionistic Fuzzy Optimization Technique

Abstract: In this paper, we develop an intuitionistic fuzzy optimization (IFO) approach for optimizing the design of plane truss structure with multiple objectives subject to a specified set of constraints. In this optimum design formulation, the objective functions are the weight of the truss and the deflection of loaded joint; the design variables are the cross-sections of the truss members; the constraints are the stresses in members. A classical truss optimization example is presented here in to demonstrate the efficiency of the Intuitionistic fuzzy optimization approach. The test problem includes a three-bar planar truss subjected to a single load condition. This multi- objective structural optimization model is solved by fuzzy optimization approach as well as intuitionistic fuzzy optimization approach. Numerical example is given to illustrate our approach. The result shows that the IFO approach is very efficient in finding the best discovered optimal solutions..

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

Abstract: The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

OpenMP Teaching-Learning Based Optimization Algorithm over Multi-Core System

Abstract: The problem with metaheuristics, including Teaching-Learning-Based Optimization (TLBO) is that, it increases in the number of dimensions (D) leads to increase in the search space which increases the amount of time required to find an optimal solution (delay in convergence). Nowadays, multi-core systems are getting cheaper and more common. To solve the above large dimensionality problem, implementation of TLBO on a multi-core system using OpenMP API's with C/C++ is proposed in this paper. The functionality of a multi- core system is exploited using OpenMP which maximizes the CPU (Central Processing Unit) utilization, which was not considered till now. The experimental results are compared with a sequential implementation of Simple TLBO (STLBO) with Parallel implementation of STLBO i.e. OpenMP TLBO, on the basis of total run time for standard benchmark problems by studying the effect of parameters, viz. population size, number of cores, dimension size, and problems of differing complexities. Linear speedup is observed by proposed OpenMP TLBO implementation over STLBO.

Multi Criterion Decision Making using Intuitionistic Fuzzy Rough Set on Two Universal Sets

Abstract: Convergence of information and communication technology has brought a radical change in the way data are collected or generated for ease of multi criterion decision making. The huge data is of no use unless it provides certain information. It is very tedious to select a best option among an array of alternatives. Also, it becomes more tedious when the data contains uncertainties and objectives of evaluation vary in importance and scope. Unlocking the hidden data is of no use to gain insight into customers, markets and organizations. Therefore, processing these data for obtaining decisions is of great challenge. Based on decision theory, in the past many methods are introduced to solve multi criterion decision making problem. The limitation of these approaches is that, they consider only certain information of the weights and decision values to make decisions. Alternatively, it makes less useful when managing uncertain and vague information. In addition, an information system establishes relation between two universal sets. In such situations, multi criterion decision making is very challenging. Therefore, an effort has been made in this paper to process inconsistencies in data with the introduction of intuitionistic fuzzy rough set theory on two universal sets.

Mobile Robot Path Planning by RRT* in Dynamic Environments

Abstract: Robot navigation is challenging for mobile robots technology in environments with maps Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used The algorithm is based on an incremental sampling which covers the whole space and acts fast Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances

Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

Abstract: Hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.

Structural Identification of Nonlinear Dynamic Systems

Abstract: The method of structural identification nonlinear dynamic systems is offered in the conditions of uncertainty. The method of construction the set containing the data about a nonlinear part of system is developed. The concept of identifiability system for a solution of a problem structural identification is introduced. The special class of structures S for a solution of problem identification is introduced. We will show that the system is identified, if the structure S is closed. The method of estimation the class of nonlinear functions on the basis of the analysis sector sets for the offered structure S is described. We showed, as on S a preliminary conclusion about a form of nonlinear function to make. We offer algorithms of structural identification of single-valued and many-valued nonlinearities. Examples of structural identification of nonlinear systems are considered.

Fractional Order EOQ Model with Linear Trend of Time-Dependent Demand

Abstract: In this paper we introduce the classical EOQ model with a linear trend of time-dependent demand having no shortages using the concept of fractional calculus. The application of fractional calculus has been already used in classical EOQ model where the demand is assumed to be constant. In this present article fractional differential calculus can be used to describe EOQ model with time-dependent linear trend of demand to develop more generalized EOQ model. Here, we want to discuss more deeply its role as a tool for describing the traditional classical EOQ model with time dependent demand.

Self Adaptive Trust Model for Secure Geographic Routing in Wireless Sensor Networks

Abstract: The presence of malicious nodes in the ad hoc and sensor networks poses serious security attacks during routing which affects the network performance. To address such attacks, numerous researchers have proposed defense techniques using a human behavior pattern called trust. Among existing solutions, direct observations based trust models have gained significant attention in the research community. In this paper, the authors propose a Self Adaptive Trust Model (SATM) of secure geographic routing in wireless sensor networks (WSNs). Unlike conventional weight based trust models, SATM intelligently assigns the weights associated with the network activities. These weights are applied to compute the final trust value. SATM considers direct observations to restrict the reputation based attacks. Due to the flexible and intelligent weight computation, SATM dynamically detects the malicious nodes and direct the traffic towards trustworthy nodes. SATM has been incorporated into Greedy Perimeter Stateless Routing (GPSR) protocol. Simulation results using the network simulator NS-2 have shown that GPSR with SATM is robust against detecting malicious nodes.

Support-Intuitionistic Fuzzy Set: A New Concept for Soft Computing

Abstract: Today, soft computing is a field that is used a lot in solving real-world problems, such as problems in economics, finance, banking... With the aim to serve for solving the real problem, many new theories and/or tools which were proposed, improved to help soft computing used more efficiently. We can mention some theories as fuzzy sets theory (L. Zadeh, 1965), intuitionistic fuzzy set (K Atanasov, 1986). In this paper, we introduce a new notion of support-intuitionistic fuzzy (SIF) set, which is the combination a intuitionistic fuzzy set with a fuzzy set. So, SIF set is a directly extension of fuzzy set and intuitionistic fuzzy sets (Atanassov). Then, we define some operators on support-intuitionistic fuzzy sets, and investigate some properties of these operators.

Design of Longitudinal Motion Controller of a Small Unmanned Aerial Vehicle

Abstract: The need for autonomous Unmanned Aerial Vehicles (UAVs) is very interesting nowadays. Autonomous UAVs provide the possibility of performing tasks and missions that are currently hazardous or can cost humans or money, enable autonomous search, persistent combat intelligence, surveillance and reconnaissance (ISR), and many other applications. This paper presents an overview of autopilot design with a detailed design of longitudinal autopilot of a Small Unmanned Aerial Vehicle (SUAV). The designed autopilot is applied to an Ultrastick-25e fixed wing UAV depending on longitudinal linear model and analytic linear model with trimmed values of straight and leveling scenario. The longitudinal motion controller design is started with the design of most inner loop (pitch rate feedback) of the longitudinal system, then pitch tracker design with a Proportional Integral (PI)controller. The guidance and control system is related with the design of altitude hold controller with Pcontroller as an example of outer loop controller design. The performance of two classic controller approaches for the design of autopilot are compared and evaluated for both linear and non-linear models. The proposed controller is chosen for design due to its higher performance than the classic one. At last the climbing turn scenario is applied to the whole autopilot (longitudinal and lateral) for the evaluation process. The results show a good performance in both disturbance rejection and robustness against sensors noise.

A Framework for Detecting Cloning Attacks in OSN Based on a Novel Social Graph Topology

Abstract: Online Social Networks (OSN) are considering one of the most popular internet applications which attract millions of users around the world to build several social relationships. Emerging the Web 2.0 technology allowed OSN users to create, share, or exchange types of contents in a popular fashion. The other hand, OSN are considering one of the most popular platforms for the intruders to spread several types of OSN attacks. Creating fake profiles for launching cloning attacks is one of the most risky attacks which target Users' profiles in Online Social Networks, the attacker seek to impersonate user's identity through duplicating user's online presence in the same or across several social networks, therefore, he can deceive OSN users into forming trusting social relations with his created fake profiles. These malicious profiles aim to harvest sensitive user's information or misuse the reputation of the legitimate profile's owner, as well as it may be used as a spy profiles for other criminal parties. Detecting these fake profiles still represent a major problem from OSN Security and Privacy point of view. In this paper we introduced a theoretical framework which depends on a novel topology of a social graph called Trusted Social Graph (TSG) which used to visualize trusted instances of social communications between OSN users. Another contribution is a proposed detection model that based on TSG topology as well as two techniques; Deterministic Finite Automaton (DFA) and Regular Expression. Our proposed detection model used to recognize the stranger instances of communications and social actions that performed using fake profiles in OSN.

Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

Abstract: Computer vision applications face various challenges while detection and classification of objects in real world like large variation in appearances, cluttered back ground, noise, occlusion, low illumination etc.. In this paper a Wavelet based Histogram of Oriented Gradients (WHOG) feature descriptors are proposed to represent shape information by storing local gradients in image. This results in enhanced representation of shape information. The performance of the feature descriptors are tested on multiclass image data set having partial occlusion, different scales and rotated object images. The performance of WHOG feature based object classification is compared with HOG feature based classification. The matching of test image with its learned class is performed using Back Propagation Neural Network (BPNN) algorithm. Proposed features not only performed superior than HOG but also beat wavelet, moment invariant and Curvelet.

Modelling and Simulation of Hydro Power Plant using MATLAB & WatPro 3.0

Abstract: Increase in demand of electricity and clean drinking water has produced a chronic need of a promising and reliable technology for the supply of both commodities, which should be entirely based on renewable sources of energy. The authors, in their previous work, had proposed a design of a hybrid power plant which used graphene membrane for power generation using reverse osmosis process. The proposal included removal of arsenic, poorly biodegradable pollutants using TiO2 nanoparticles. Chlorine production using the process of electrolysis. The plant was also electronically implemented and included pump control, fouling detection modules and decision module for the volume of effluents to be discharged. The performance of a power system is essential to be analyzed for control, stabilization and efficient modelling. In the present research paper, simulation model of the hybrid plant is analyzed. The chemical behavior is analyzed with 'Watpro 3.0' industrial software and turbine governance system is studied via MATLAB. This plant is a potential replacement of chemical purification techniques with high overhead and excess cost. It is a better, efficient, safe and reliable system to produce clean and safe drinking water and electricity simultaneously. I. INTRODUCTION One of the major problem in present scenario is power generation, which is the root cause of depletion of fossil fuel. An efficient and effective techno-economic renewable power generation techniques is proposed for trapping energy which is released during the mixing of seawater and freshwater. In consideration of centralized and decentralized frameworks for drinking water regulation in the context of risk management theory and practical challenges. The second most critical problem analyzed here is purification of contaminated water. Chemical purification is analyzed in detail in the paper. The proposed hybrid power and purification plant could be the key for a regular and safe supply of drinking water as well as power. This plant is a boon for regions of the world stricken with water or power scarcity and water contamination. The basic architecture of the proposed plant model is shown in figure 1.

Individually Directional Evolutionary Algorithm for Solving Global Optimization Problems-Comparative Study

Abstract: Limited applicability of classical optimization methods influence the popularization of stochastic optimization techniques such as evolutionary algorithms (EAs). EAs are a class of probabilistic optimization techniques inspired by natural evolution process, witch belong to methods of Computational Intelligence (CI). EAs are based on concepts of natural selection and natural genetics. The basic principle of EA is searching optimal solution by processing population of individuals. This paper presents the results of simulation analysis of global optimization of benchmark function by Individually Directional Evolutionary Algorithm (IDEA) and other EAs such as Real Coded Genetic Algorithm (RCGA), elite RCGA with the one elite individual, elite RCGA with the number of elite individuals equal to population size. IDEA is a newly developed algorithm for global optimization. Main principle of IDEA is to monitor and direct the evolution of selected individuals of population to explore promising areas in the search space. The idea of IDEA is an independent evolution of individuals in current population. This process is focused on indicating correct direction of changes in the elements of solution vector. This paper presents a flowchart, selection method and genetic operators used in IDEA. Moreover, similar mechanisms and genetic operators are also discussed.

Structural Methods of Estimation Lyapunov Ex- ponents Linear Dynamic System

Abstract: The problem of structural identification linear dynamic systems on the basis of the analysis Lyapunov exponent in the conditions of uncertainty is considered The method of estimation the general solution system on the basis of application static model is developed Defini- tion of Lyapunov exponent (LE) on the analysis of a co- efficient structural properties system is grounded On the basis of a coefficient of structural properties the special structures reflecting change LE are introduced The crite- rion of estimation an order system on the basis of the analysis behaviour these structures are offered The deci- sion-making method about type of roots dynamic system on the basis of the analysis of time series and the struc- tures reflecting change LE is developed Two approaches to an estimation of the largest LE and the Perron bottom indexes are offered The first approach to identification of a change in a coefficient of structural properties with the help secant method for various classes of roots is ground- ed The second approach is the structurally-frequency method grounded on definition of estimations LE by means of the analysis of local minima of structures of- fered in work The frequency method which is a modifi- cation of a method a bar graph in the statistical theory is applied to a validation of the obtained estimations Re- sults of simulation confirm effectiveness of the offered methods, structures and procedures

Producer-Scrounger Method to Solve Traveling Salesman Problem

Abstract: Algorithms inspired from natural phenomena are seem to be efficient to solve various optimization problems. This paper investigates a new technique inspiring from the animal group living behavior to solve traveling salesman problem (TSP), the most popular combinatorial optimization problem. The proposed producer-scrounger method (PSM) models roles and interactions of three types of animal group members: producer, scrounger and dispersed. PSM considers a producer having the best tour, few dispersed members having worse tours and scroungers. In each iteration, the producer scans for better tour, scroungers explore new tours while moving toward producer's tour; and dispersed members randomly checks new tours. For producer's scanning, PSM randomly selects a city from the producer's tour and rearranges its connection with several near cities for better tours. Swap operator and swap sequence based operation is employed in PSM to update a scrounger towards the producer. The proposed PSM has been tested on a large number of benchmark TSPs and outcomes compared to genetic algorithm and ant colony optimization. Experimental results revealed that proposed PSM is a good technique to solve TSP providing the best tours in most of the TSPs.

Intelligent Approaches to Real Time Level Control

Abstract: Liquid level control is important for ensuring energy and material balance in many installations but it also difficult as the plant is nonlinear, inertial and with model uncertainties Fuzzy logic controllers (FLCs) are successfully applied to ensure system stability and robustness by simple means and a model-free design This paper suggests a procedure for off-line tuning of the many FLC parameters based on optimization of a suggested multi-objective function defined on several system performance indices using genetic algorithms (GAs) First, a model-free FLC is empirically tuned, then applied for real time control of the plant and the necessary data recorded and used to GA parameter optimize a TSK plant model of an accepted structure The validated on different set of experimental data model is employed in FLC closed loop system simulation experiments to evaluate the fitness function in the GA optimization of the FLC pre-processing and post- processing parameters The procedure is applied for the real time PI/PID FLC level control in a laboratory-scale tank system The improvement of the system performance indices due to the GA optimization is estimated in level real time control

An Efficient Algorithm in Mining Frequent Itemsets with Weights over Data Stream Using Tree Data Structure

Abstract: In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds. In [20], a framework for mining frequent itemsets over a data stream is proposed by the use of weighted slide window model. Two algorithms of single pass (WSW) and the WSW-Imp (improving one) using weighted sliding model were proposed in there to solve the data stream problems. The disadvantage of these algorithms is that they have to seek all data stream many times and generate a large set of candidates. In this paper, we have proposed a process of mining frequent itemsets with weights over a data stream. Based on the downward closure property and FPGrowth method [8, 9] an alternative algorithm called WSWFP-stream has been proposed. This algorithm is proved working more efficiently regarding to computing time and memory aspects.