SAI - 2014

Accepted Papers

Combination of reinforcement learning and braitenberg techniques for faster mobile robot navigation
Javad.M.Marzbali and Mohsen Nikpour,Mazandaran Institute of Technology,Iran
ABSTRACT
Machine learning proceed asbroad and useful branch of artificial intelligence to adjust and explore of ways and algorithms that based they computers and systems are found the training and learning ability.Expanding technology-based robotics, will inevitable sagacity these machines. Therefor reinforcement learning as a powerful method for machine learning,will be Significant role in increasing the robot performance quality and improving their behaviors.In this paper, for achieve soft and efficient navigation in unknown environments for mobile robots with two fixed wheels, an algorithm is presented.This algorithm utilize combine of Braitenberg machine idea with reinforcement learning that for example is run on E-puck robot.First, the introduction of reinforcement learning is discussed,And in continuation in order to checking the efficiency of the navigation algorithm and also for that govern on the issue existent Terms and Conditions in the real world,This algorithm is considering beneficial of combination of reinforcement learning and Braitenberg techniques in Webots simulation software has been implemented and tested.also,In this study, the Matlab software has been used for implement the algorithm to the robot.
An Investigation On Switching Behaviours Of Vector Controlled Induction Motors
Fatih Korkmaz, Ismail Topaloglu, Hayati Mamur and Murat Ari,Cankırı Karatekin University,Turkey
ABSTRACT
Field oriented control and direct torque control are the most popular methods in high performance industrial control applications for induction motors. Naturally, the strengths and weaknesses of each control method are available. Therefore, the selection of optimum control method is vitally important for many industrial applications. So, the advantages and the disadvantages of both control methods have to be well defined. In this paper, a new and different perspective has been presented regarding the comparison of the inverter switching behaviours on the FOC and the DTC drivers. For this purpose, the experimental studies have been carried out to compare the inverter switching frequencies and torque responses of induction motors in the FOC and the DTC systems. The dSPACE 1103 controller board has been programmed with Matlab/Simulink software. As expected, the experimental studies have showed that the FOC controlled motors have had a lessened torque ripple. On the other hand, the FOC controlled motor switching frequency has about 75% more than the DTC controlled.
Intelligent Adaptive Learning in a Changing Environment
G.Valentis and Q.Berthelot,ECE Paris Graduate School of Engineering,France
ABSTRACT
Nowadays the trend to develop ever more intelligent and autonomous systems often takes its inspiration in the living beings on Earth. Some simple isolated systems are able, once brought together, to form a strong and reliable system. When trying to adapt the idea to man-made systems it is not possible to include in their program everything the system may encounter during its life cycle. It is thus necessary to make the system able to take decisions based on other criteria such as its past experience, ie to make the system learn on its own. However at some point the acquired knowledge depends also on environment. So the question is: if system environment is modified, how could the system respond to it quickly and appropriately enough? Here, starting from reinforcement learning to rate its decisions, and using adaptive learning algorithms for gain and loss reward, the system is made able to respond to changing environment and to adapt its knowledge as time passes. Application is made to a robot finding an exit in a labyrinth.
Bessel Features for Malayalam Language based Speaker Identification
Drisya Vasudev and Anish Babu K.K,Rajiv Gandhi Institute Of Technology,India
ABSTRACT
Speaker identification attempts to determine the best possible match from a group of certain speakers, for any given input speech signal. The text-independent speaker identification system does the task to identify the person who speaks regardless of what is said. The first step in speaker identification is the extraction of features. In this proposed method, the Bessel features are used as an alternative to the popular techniques like MFCC and LPCC. The quasi-stationary nature of speech signal is more efficiently represented by damped sinusoidal basis function that is more natural for the voiced speech signal. Since Bessel functions have damped sinusoidal as basis function, it is more natural choice for the representation of speech signals. Here, Bessel features derived from the speech signal is used for creating the Gaussian mixture models for text independent speaker identification. A set of ten speakers is used for modelling using Gaussian mixtures. The proposed system is made to test over the Malayalam database obtaining an efficiency of 98% which is promising.
Performance Analysis of the Recent Role of OMSA Approaches in Online Social Networks
J.Ashok Kumar and S.Abirami,Anna University,India
ABSTRACT
In this emerging trend, it is necessary to understand the recent developments taking place in the field of opinion mining and sentiment analysis (OMSA) as part of text mining in social networks, which plays an important role for decision making process to the organization or company, Government and general public. In this paper, we present the recent role of OMSA in Social Networks with different frameworks such as data collection process, text pre-processing, classification algorithms, and performance evaluation results. The achieved accuracy level is compared and shown for different frameworks. Finally, we conclude the present challenges and future developments of OMSA.
Controlling A Humanoid Robot Arm For Grasping And Manipulating A Moving Object In The Presence Of Obstacles Without Cameras
Ali Chaabani¹,Mohamed Sahbi Bellamine² and Moncef Gasmi²,¹University of Manar,Tunisia,²University of Carthage,Tunisia
ABSTRACT
Lot of researchers working in robotic grasping tasks assume a stationary or fixed object, others have focused on dynamic moving objects using cameras to record images of the moving object and then they treated their images to estimate the position to grasp it. This method is quite difficult requiring a lot of computing, image processing. Hence, we must seek a more simple handling method. Moreover, the majorities of robotic arms available for humanoid applications are complex to control and are expensive. In this paper, we explore the requirements for controlling a humanoid robot arm with 7 degree-offreedom to grasp and handle any moving objects in 3-D environment with or without obstacles and without using the cameras. We used the OpenRAVE simulation environment and a robot arm equipped with the Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an extension of RRT-JT that interleaves exploration using a Rapidly-exploring Random Tree with exploitation using Jacobian-based gradient descent to control a 7-DoF WAM robotic arm to avoid the obstacles, track a moving object, and grasp planning. We present results in which a moving mug is tracked and stably grasped with a maximum rate of success in a reasonable time and picked up by the Barret hand to a desired position.
Optimal Buffer Allocation in Tandem Closed Queuing Network with Multi Servers using PSO
K.L.Narasimhamu¹ V.Venugopal Reddy² C.S.P.Rao³ ,¹ Annamacharya Institute of Technology & Sciences,India,² JNTUA College of Engineering,India,³ National Institute of Technology,India
ABSTRACT
Buffer Allocation Problem is an important research issue in manufacturing system design. Objective of this paper is to find optimum buffer allocation for closed queuing network with multi servers at each node. Sum of buffers in closed queuing network is constant. Attempt is made to find optimum number of pallets required to maximize throughput of manufacturing system which has pre specified space for allocating pallets. Expanded Mean Value Analysis is used to evaluate the performance of closed queuing network. Particle Swarm Optimization is used as generative technique to optimize the buffer allocation. Numerical experiments are shown to explain effectiveness of procedure.
An Improved Teaching-Learning Based optimization Approach for Fuzzy Clustering
Parastou Shahsamandi E. and Soheil Sadi-nezhad,Islamic Azad University,Iran
ABSTRACT
Fuzzy clustering has been widely studied and applied in a variety of key areas of science and engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO) algorithm is used for data clustering, in which the objects in the same cluster are similar. This algorithm has been tested on several datasets and compared with some other popular algorithm in clustering. Results have been shown that the proposed method improves the output of clustering and can be efficiently used for fuzzy clustering.
Comparison Of Filtering And Clustering Techniques In Diagnosis Of Infants Retinopathy Risk
Niousha Hormozi Seyed Amirhassan Monadjemi and Gholamali Naderian,Isfahan University,Iran
ABSTRACT
ROP is an eye disease in premature infants. In infants who are born earlier than normal, retinal vessel growth stops. Early treatment is very crucial in this case as it can end up to blindness if the diagnosis has not done in a short time. The purpose of this research is to design an intelligent automated system for the early detection of disease at an early stage to prevent these babies from dangerous consequences. In this study, we analyzed the images in the Lab color space, and evaluated the efficiency of applying filters named, Canny Laplacian and Sobel. The results indicate relatively higher efficiency and quality of the Laplacian filter in ROP diagnosis.
A Modified Invasive Weed Optimization Algorithm for Multiobjective Flexible Job Shop Scheduling Problems
Souad Mekni and Besma Chaar Fayech,National School of Engineering of Tunis,Tunisia
ABSTRACT
In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.
Iranian Cashes Recognition Using Mobile
Ismail Nojavani,Amir Hassan Monadjemi and Azade Rezaeezade,Isfahan University,Iran
ABSTRACT
In economical societies of today, using cash is an inseparable aspect of human life. People use cashes for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing the monetary value of it caused one of the most challenging problems for visually impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a picture taken from cashes using some image processing and machine vision techniques. While the developed approach is very fast, it can recognize the value of cash by average accuracy of about 95% and can overcome different challenges like rotation, scaling, collision, illumination changes, perspective, and some others.
An Automated License Plate Recognition System for Moving Vehicles
Hitesh Rajput¹, Tanmoy Som¹, Somitra Kar²,¹Indian Institute of Technology (Banaras Hindu University),India,²Bhabha Atomic Research Centre,India
ABSTRACT
The need of Vehicular License Plate Recognition system (VLPR) has arose based on the need to implement traffic control on transportation systems, since early 1970's. Since then, researchers are continuously proposing various approaches and solutions. One of the significant and challenging tasks is to localize the license plate of the moving car. Since the license plate standards are not strictly practiced in world, a large amount of variations are obtained like, size, location, type of font used, background and foreground color and so on. The Principal Component Analysis (PCA) is one of the widely used and most successful techniques that have been used in image recognition and compression. In this paper we propose a novel approach to localize the license plate using PCA.
URWF: User Reputation based Weightage Framework for Twitter Micropost Classification
Asad Bukhari¹,Usman Qamar²,Um-e-Ghazia³,¹College of Electrical and Mechanical Engineering,Pakistan,²National University of Sciences & Technology,Pakistan,³School of Electrical Engineering and Computer Science, NUST,Pakistan
ABSTRACT
Sentiment analysis is an emerging field that helps in understanding the sentiments of user on microblogging sites. Many sentiment analysis techniques have been proposed by researchers that classifies and analyze the sentiments from micropost posted by various users. Majorly, these techniques perform text based classification that does not allow predicting the micropost impact. Further it is very difficult to analyze this huge volume of online content produced each day. Therefore, an effective technique for sentiment analysis is required that not only perform the precise text based classification but also makes the analysis easy by reducing the volume of data. Moreover, micropost impact must also be determined in order to segregates the high impact microposts in corpus. In the present study, we have presented sentiment analysis framework that incorporates any text based classification and separates out the high impact microposts from low impact by calculating the factor of user reputation. This user reputation is calculated by considering multiple factors regarding user activities that help business to know about customer opinions and views for their products and services. This way volume of data becomes small that has to be analyzed by considering only microposts posted by high impact users. Multiple text classifications classes are introduced instead of just positive, negative and neutral for precise sentiment classification. The proposed framework also calculates the accumulated weight of each micropost by multiplying the user reputation with the assigned sentiment score. The user reputation calculation factors are validated by using Spearman rho and Kendall tau correlation coefficient. The framework is further evaluated by using the Sanders topic based corpus and results are presented.
An Immune Agents System For network Intrusions Detection
Noria Benyettou¹, Abdelkader Benyettou¹ and Vincent Rodin², ¹University of Science and Technology of Oran Mohamed Boudiaf USTOMB, SIMPA Laboratory, Algeria and ²European University of Brittany, France
ABSTRACT
With the development growing of network technology, the computer networks became increasingly wide and opened. This evolution gave birth to new technics allowing the accessibility of the networks and information systems with an aim of facilitating the transactions. Consequently, these techniques gave also birth to new forms of threats. In this article, we present the utility to used a system of intrusion detection through a presentation of these characteristics, followed by a brief history of the existing models. Using as inspiration the immune biological system, we propose a model of artificial immune system which is integrated in the behavior of distributed agents on the network in order to ensure a good detection of intrusions. We also present the internal structure of the immune agents and their capacity to distinguish between self and not self. The agents are able to achieve simultaneous treatments, are able to auto-adaptable to the evolution of environment and have also the property of distributed coordination. In this architecture, the immune agent of model is installed in each host of the network and subnetwork, for an extensive monitoring and a simultaneous analysis of the frames.
An Agent Based Resource Discovery For Peak Request Periods In Peer-To-Peer Grid Infrastructures.
Moses Olaifa¹and Temitope Mapayi²,¹University of South Africa,South Africa,²University of Kwazulu-Natal,South Africa
ABSTRACT
One of the fundamentally required services in the grid environment is resource discovery. The discovery involves the search for appropriate resources that match user requirements. An efficient mechanism for this service still remains a crucial problem especially within a dynamic and scalable environment such as the grid. Majority of the proposed solutions based on centralized and hierarchical approaches suffer from shortcomings ranging from single point of failure to network congestion. In this paper, we propose a resource discovery mechanism that relies on the activities of an agent during peak request hours in a peer-to-peer (P2P) based grid system. The agent searches and learns the paths to requested resources with associated maximum rewards. These paths are managed by the super-node for subsequent resource discovery requests. We evaluated the performance of the proposed approach against some resource discovery approaches. The results show an improved performance in the proposed algorithm over the TTL and GAA.
Exploring Folksonomy structure for personalizing the result merging process in distributed information retrieval
Zakaria Saoud and Samir Kechid and Radia Amrouni,University of Sciences and Technologies Houari Boumediene,Algeria
ABSTRACT
In this paper we present a new personalized approach that integrates a social profile in distributed search system. The proposed approach exploits the social profile and the different relations between social entities to : (i) make a query expansion, (ii) personalize and improve the result merging process in distributed information retrieval.



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Home Paper Submission Program Committee Accepted Papers Contact Us	Accepted Papers Combination of reinforcement learning and braitenberg techniques for faster mobile robot navigation Javad.M.Marzbali and Mohsen Nikpour,Mazandaran Institute of Technology,Iran ABSTRACT Machine learning proceed asbroad and useful branch of artificial intelligence to adjust and explore of ways and algorithms that based they computers and systems are found the training and learning ability.Expanding technology-based robotics, will inevitable sagacity these machines. Therefor reinforcement learning as a powerful method for machine learning,will be Significant role in increasing the robot performance quality and improving their behaviors.In this paper, for achieve soft and efficient navigation in unknown environments for mobile robots with two fixed wheels, an algorithm is presented.This algorithm utilize combine of Braitenberg machine idea with reinforcement learning that for example is run on E-puck robot.First, the introduction of reinforcement learning is discussed,And in continuation in order to checking the efficiency of the navigation algorithm and also for that govern on the issue existent Terms and Conditions in the real world,This algorithm is considering beneficial of combination of reinforcement learning and Braitenberg techniques in Webots simulation software has been implemented and tested.also,In this study, the Matlab software has been used for implement the algorithm to the robot. An Investigation On Switching Behaviours Of Vector Controlled Induction Motors Fatih Korkmaz, Ismail Topaloglu, Hayati Mamur and Murat Ari,Cankırı Karatekin University,Turkey ABSTRACT Field oriented control and direct torque control are the most popular methods in high performance industrial control applications for induction motors. Naturally, the strengths and weaknesses of each control method are available. Therefore, the selection of optimum control method is vitally important for many industrial applications. So, the advantages and the disadvantages of both control methods have to be well defined. In this paper, a new and different perspective has been presented regarding the comparison of the inverter switching behaviours on the FOC and the DTC drivers. For this purpose, the experimental studies have been carried out to compare the inverter switching frequencies and torque responses of induction motors in the FOC and the DTC systems. The dSPACE 1103 controller board has been programmed with Matlab/Simulink software. As expected, the experimental studies have showed that the FOC controlled motors have had a lessened torque ripple. On the other hand, the FOC controlled motor switching frequency has about 75% more than the DTC controlled. Intelligent Adaptive Learning in a Changing Environment G.Valentis and Q.Berthelot,ECE Paris Graduate School of Engineering,France ABSTRACT Nowadays the trend to develop ever more intelligent and autonomous systems often takes its inspiration in the living beings on Earth. Some simple isolated systems are able, once brought together, to form a strong and reliable system. When trying to adapt the idea to man-made systems it is not possible to include in their program everything the system may encounter during its life cycle. It is thus necessary to make the system able to take decisions based on other criteria such as its past experience, ie to make the system learn on its own. However at some point the acquired knowledge depends also on environment. So the question is: if system environment is modified, how could the system respond to it quickly and appropriately enough? Here, starting from reinforcement learning to rate its decisions, and using adaptive learning algorithms for gain and loss reward, the system is made able to respond to changing environment and to adapt its knowledge as time passes. Application is made to a robot finding an exit in a labyrinth. Bessel Features for Malayalam Language based Speaker Identification Drisya Vasudev and Anish Babu K.K,Rajiv Gandhi Institute Of Technology,India ABSTRACT Speaker identification attempts to determine the best possible match from a group of certain speakers, for any given input speech signal. The text-independent speaker identification system does the task to identify the person who speaks regardless of what is said. The first step in speaker identification is the extraction of features. In this proposed method, the Bessel features are used as an alternative to the popular techniques like MFCC and LPCC. The quasi-stationary nature of speech signal is more efficiently represented by damped sinusoidal basis function that is more natural for the voiced speech signal. Since Bessel functions have damped sinusoidal as basis function, it is more natural choice for the representation of speech signals. Here, Bessel features derived from the speech signal is used for creating the Gaussian mixture models for text independent speaker identification. A set of ten speakers is used for modelling using Gaussian mixtures. The proposed system is made to test over the Malayalam database obtaining an efficiency of 98% which is promising. Performance Analysis of the Recent Role of OMSA Approaches in Online Social Networks J.Ashok Kumar and S.Abirami,Anna University,India ABSTRACT In this emerging trend, it is necessary to understand the recent developments taking place in the field of opinion mining and sentiment analysis (OMSA) as part of text mining in social networks, which plays an important role for decision making process to the organization or company, Government and general public. In this paper, we present the recent role of OMSA in Social Networks with different frameworks such as data collection process, text pre-processing, classification algorithms, and performance evaluation results. The achieved accuracy level is compared and shown for different frameworks. Finally, we conclude the present challenges and future developments of OMSA. Controlling A Humanoid Robot Arm For Grasping And Manipulating A Moving Object In The Presence Of Obstacles Without Cameras Ali Chaabani¹,Mohamed Sahbi Bellamine² and Moncef Gasmi²,¹University of Manar,Tunisia,²University of Carthage,Tunisia ABSTRACT Lot of researchers working in robotic grasping tasks assume a stationary or fixed object, others have focused on dynamic moving objects using cameras to record images of the moving object and then they treated their images to estimate the position to grasp it. This method is quite difficult requiring a lot of computing, image processing. Hence, we must seek a more simple handling method. Moreover, the majorities of robotic arms available for humanoid applications are complex to control and are expensive. In this paper, we explore the requirements for controlling a humanoid robot arm with 7 degree-offreedom to grasp and handle any moving objects in 3-D environment with or without obstacles and without using the cameras. We used the OpenRAVE simulation environment and a robot arm equipped with the Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an extension of RRT-JT that interleaves exploration using a Rapidly-exploring Random Tree with exploitation using Jacobian-based gradient descent to control a 7-DoF WAM robotic arm to avoid the obstacles, track a moving object, and grasp planning. We present results in which a moving mug is tracked and stably grasped with a maximum rate of success in a reasonable time and picked up by the Barret hand to a desired position. Optimal Buffer Allocation in Tandem Closed Queuing Network with Multi Servers using PSO K.L.Narasimhamu¹ V.Venugopal Reddy² C.S.P.Rao³ ,¹ Annamacharya Institute of Technology & Sciences,India,² JNTUA College of Engineering,India,³ National Institute of Technology,India ABSTRACT Buffer Allocation Problem is an important research issue in manufacturing system design. Objective of this paper is to find optimum buffer allocation for closed queuing network with multi servers at each node. Sum of buffers in closed queuing network is constant. Attempt is made to find optimum number of pallets required to maximize throughput of manufacturing system which has pre specified space for allocating pallets. Expanded Mean Value Analysis is used to evaluate the performance of closed queuing network. Particle Swarm Optimization is used as generative technique to optimize the buffer allocation. Numerical experiments are shown to explain effectiveness of procedure. An Improved Teaching-Learning Based optimization Approach for Fuzzy Clustering Parastou Shahsamandi E. and Soheil Sadi-nezhad,Islamic Azad University,Iran ABSTRACT Fuzzy clustering has been widely studied and applied in a variety of key areas of science and engineering. In this paper the Improved Teaching Learning Based Optimization (ITLBO) algorithm is used for data clustering, in which the objects in the same cluster are similar. This algorithm has been tested on several datasets and compared with some other popular algorithm in clustering. Results have been shown that the proposed method improves the output of clustering and can be efficiently used for fuzzy clustering. Comparison Of Filtering And Clustering Techniques In Diagnosis Of Infants Retinopathy Risk Niousha Hormozi Seyed Amirhassan Monadjemi and Gholamali Naderian,Isfahan University,Iran ABSTRACT ROP is an eye disease in premature infants. In infants who are born earlier than normal, retinal vessel growth stops. Early treatment is very crucial in this case as it can end up to blindness if the diagnosis has not done in a short time. The purpose of this research is to design an intelligent automated system for the early detection of disease at an early stage to prevent these babies from dangerous consequences. In this study, we analyzed the images in the Lab color space, and evaluated the efficiency of applying filters named, Canny Laplacian and Sobel. The results indicate relatively higher efficiency and quality of the Laplacian filter in ROP diagnosis. A Modified Invasive Weed Optimization Algorithm for Multiobjective Flexible Job Shop Scheduling Problems Souad Mekni and Besma Chaar Fayech,National School of Engineering of Tunis,Tunisia ABSTRACT In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied. Iranian Cashes Recognition Using Mobile Ismail Nojavani,Amir Hassan Monadjemi and Azade Rezaeezade,Isfahan University,Iran ABSTRACT In economical societies of today, using cash is an inseparable aspect of human life. People use cashes for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing the monetary value of it caused one of the most challenging problems for visually impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a picture taken from cashes using some image processing and machine vision techniques. While the developed approach is very fast, it can recognize the value of cash by average accuracy of about 95% and can overcome different challenges like rotation, scaling, collision, illumination changes, perspective, and some others. An Automated License Plate Recognition System for Moving Vehicles Hitesh Rajput¹, Tanmoy Som¹, Somitra Kar²,¹Indian Institute of Technology (Banaras Hindu University),India,²Bhabha Atomic Research Centre,India ABSTRACT The need of Vehicular License Plate Recognition system (VLPR) has arose based on the need to implement traffic control on transportation systems, since early 1970's. Since then, researchers are continuously proposing various approaches and solutions. One of the significant and challenging tasks is to localize the license plate of the moving car. Since the license plate standards are not strictly practiced in world, a large amount of variations are obtained like, size, location, type of font used, background and foreground color and so on. The Principal Component Analysis (PCA) is one of the widely used and most successful techniques that have been used in image recognition and compression. In this paper we propose a novel approach to localize the license plate using PCA. URWF: User Reputation based Weightage Framework for Twitter Micropost Classification Asad Bukhari¹,Usman Qamar²,Um-e-Ghazia³,¹College of Electrical and Mechanical Engineering,Pakistan,²National University of Sciences & Technology,Pakistan,³School of Electrical Engineering and Computer Science, NUST,Pakistan ABSTRACT Sentiment analysis is an emerging field that helps in understanding the sentiments of user on microblogging sites. Many sentiment analysis techniques have been proposed by researchers that classifies and analyze the sentiments from micropost posted by various users. Majorly, these techniques perform text based classification that does not allow predicting the micropost impact. Further it is very difficult to analyze this huge volume of online content produced each day. Therefore, an effective technique for sentiment analysis is required that not only perform the precise text based classification but also makes the analysis easy by reducing the volume of data. Moreover, micropost impact must also be determined in order to segregates the high impact microposts in corpus. In the present study, we have presented sentiment analysis framework that incorporates any text based classification and separates out the high impact microposts from low impact by calculating the factor of user reputation. This user reputation is calculated by considering multiple factors regarding user activities that help business to know about customer opinions and views for their products and services. This way volume of data becomes small that has to be analyzed by considering only microposts posted by high impact users. Multiple text classifications classes are introduced instead of just positive, negative and neutral for precise sentiment classification. The proposed framework also calculates the accumulated weight of each micropost by multiplying the user reputation with the assigned sentiment score. The user reputation calculation factors are validated by using Spearman rho and Kendall tau correlation coefficient. The framework is further evaluated by using the Sanders topic based corpus and results are presented. An Immune Agents System For network Intrusions Detection Noria Benyettou¹, Abdelkader Benyettou¹ and Vincent Rodin², ¹University of Science and Technology of Oran Mohamed Boudiaf USTOMB, SIMPA Laboratory, Algeria and ²European University of Brittany, France ABSTRACT With the development growing of network technology, the computer networks became increasingly wide and opened. This evolution gave birth to new technics allowing the accessibility of the networks and information systems with an aim of facilitating the transactions. Consequently, these techniques gave also birth to new forms of threats. In this article, we present the utility to used a system of intrusion detection through a presentation of these characteristics, followed by a brief history of the existing models. Using as inspiration the immune biological system, we propose a model of artificial immune system which is integrated in the behavior of distributed agents on the network in order to ensure a good detection of intrusions. We also present the internal structure of the immune agents and their capacity to distinguish between self and not self. The agents are able to achieve simultaneous treatments, are able to auto-adaptable to the evolution of environment and have also the property of distributed coordination. In this architecture, the immune agent of model is installed in each host of the network and subnetwork, for an extensive monitoring and a simultaneous analysis of the frames. An Agent Based Resource Discovery For Peak Request Periods In Peer-To-Peer Grid Infrastructures. Moses Olaifa¹and Temitope Mapayi²,¹University of South Africa,South Africa,²University of Kwazulu-Natal,South Africa ABSTRACT One of the fundamentally required services in the grid environment is resource discovery. The discovery involves the search for appropriate resources that match user requirements. An efficient mechanism for this service still remains a crucial problem especially within a dynamic and scalable environment such as the grid. Majority of the proposed solutions based on centralized and hierarchical approaches suffer from shortcomings ranging from single point of failure to network congestion. In this paper, we propose a resource discovery mechanism that relies on the activities of an agent during peak request hours in a peer-to-peer (P2P) based grid system. The agent searches and learns the paths to requested resources with associated maximum rewards. These paths are managed by the super-node for subsequent resource discovery requests. We evaluated the performance of the proposed approach against some resource discovery approaches. The results show an improved performance in the proposed algorithm over the TTL and GAA. Exploring Folksonomy structure for personalizing the result merging process in distributed information retrieval Zakaria Saoud and Samir Kechid and Radia Amrouni,University of Sciences and Technologies Houari Boumediene,Algeria ABSTRACT In this paper we present a new personalized approach that integrates a social profile in distributed search system. The proposed approach exploits the social profile and the different relations between social entities to : (i) make a query expansion, (ii) personalize and improve the result merging process in distributed information retrieval.
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