Pattern Analysis and Intelligent Systems
Ehsan Soleimani; Kamal Mirzaie
Articles in Press, Accepted Manuscript, Available Online from 30 July 2022
Abstract
With the increasing use of online services in the form of platforms or websites, as well as the growth of users in online spaces, it seems necessary to study and analyze these networks. One of the areas of interest for social media analysts is Community Detection (CD) in these networks. The purpose of ...
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With the increasing use of online services in the form of platforms or websites, as well as the growth of users in online spaces, it seems necessary to study and analyze these networks. One of the areas of interest for social media analysts is Community Detection (CD) in these networks. The purpose of CD is to discover subcommunities within the original networks. Identifying these groups in social networks has many applications in discovering new groups, and extracting common points between users. The main challenge in CD is to find the relationships between the nodes and form clusters in them. In particular, the use of CD techniques makes it possible to identify similar nodes that have common features. Because user data does not belong to a specific category and there is a lot of data in the community and a similar relationship should be established between them based on the type of data. In this paper, a new method for CD using an Adaptive Genetic Algorithm (AGA) is proposed. The evaluation of the proposed model was performed on the Enron Email suite using Normalized Mutual Information (NMI), Variation Information (VI) and Cross Common Fraction (CCF) criteria. The results show that with increasing the number of generations, the accuracy of the proposed model increases and also the rate of crossover and mutation are very effective in the accuracy of detection. The accuracy of NMI, VI and CCF criteria with 800 iterations is 82.17, 72.95 and 75.16, respectively.
Computer Architecture and Digital Systems
Jibril Bala; Olayemi Olaniyi; Taliha Folorunso; Tayo Arulogun
Volume 6, Issue 4 , November 2020, , Pages 213-226
Abstract
Proportional-Integral-Derivative (PID) controllers and Internal Model Controllers (IMC) are effective tools in control analysis and design. However, parameter tuning, and inaccurate model representation often lead to unsatisfactory closed loop performance. In this study, we analyse the effect of PID ...
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Proportional-Integral-Derivative (PID) controllers and Internal Model Controllers (IMC) are effective tools in control analysis and design. However, parameter tuning, and inaccurate model representation often lead to unsatisfactory closed loop performance. In this study, we analyse the effect of PID controllers and IMCs tuned with Genetic Algorithm (GA) and Fuzzy Logic (FL), on a poultry feeding system. The use of GA and FL for tuning of the PID and IMC parameters was done to enhance the adaptability and optimality of the controller. A comparative analysis was made to analyse closed loop performance and ascertain the most effective controller. The results showed that the GA-PID and FL-PID gave a better performance in the aspect of rise time, settling time and Integrated Absolute Error (IAE). On the other hand, the GA-IMC and FL-IMC gave better performances in the aspect of the performance overshoot. Therefore, for processes in which a faster response and lower IAE are desired, the GA-PID and FL-PID are more effective while for processes in which the major objective is to minimise the overshoot, the GA-IMC and FL-IMC are more suitable.
Pattern Analysis and Intelligent Systems
Zeynab Sedreh; Mehdi Sadeghzadeh
Volume 5, Issue 1 , February 2019, , Pages 19-26
Abstract
In path planning Problems, a complete description of robot geometry, environments and obstacle are presented; the main goal is routing, moving from source to destination, without dealing with obstacles. Also, the existing route should be optimal. The definition of optimality in routing is the same as ...
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In path planning Problems, a complete description of robot geometry, environments and obstacle are presented; the main goal is routing, moving from source to destination, without dealing with obstacles. Also, the existing route should be optimal. The definition of optimality in routing is the same as minimizing the route, in other words, the best possible route to reach the destination. In most of the routing methods, the environment is known, although, in reality, environments are unpredictable;But with the help of simple methods and simple changes in the overall program, one can see a good view of the route and obstacles ahead. In this research, a method for solving robot routing problem using cellular automata and genetic algorithm is presented.In this method, the working space model and the objective function calculation are defined by cellular automata, and the generation of initial responses and acceptable responses is done using the genetic algorithm.During the experiments and the comparison we made, we found that the proposed algorithm yielded a path of 28.48 if the lengths of the paths obtained in an environment similar to the other algorithm of 15 / 32, 29.5 and 29.49, which is more than the proposed method.
Computer Networks and Distributed Systems
Elaheh Radmehr; HASSAN SHAKERI
Volume 3, Issue 4 , November 2017, , Pages 189-194
Abstract
Wireless sensor networks have been widely considered as one of the most important 21th century technologies and are used in so many applications such as environmental monitoring, security and surveillance. Wireless sensor networks are used when it is not possible or convenient to supply signaling or ...
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Wireless sensor networks have been widely considered as one of the most important 21th century technologies and are used in so many applications such as environmental monitoring, security and surveillance. Wireless sensor networks are used when it is not possible or convenient to supply signaling or power supply wires to a wireless sensor node. The wireless sensor node must be battery powered.Coverage and network lifetime are major problems in WSNs so in order to address this difficulty we propose a combinational method consists of fuzzy-logic and genetic algorithms. The proposed scheme detects the coverage holes in the network and selects the most appropriate hole's neighbor to move towards the blank area and compensate the coverage loss with fuzzy-logic contribution and above node new coordinate is determined by genetic algorithm. As fuzzy-logic will be so effective if more than one factor influence on decision making and also genetic algorithms perform well in dynamic problems so our proposed solution results in fast, optimized and reliable output
Computer Networks and Distributed Systems
Marzieh Bozorgi Elize; Ahmad KhademZadeh
Volume 3, Issue 4 , November 2017, , Pages 203-212
Abstract
Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, ...
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Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, in addition to high operating costs and huge amounts of electrical power consumption. Insufficient cooling systems and inefficient, causing overheating sources, shortening the life of the machine and too much carbon dioxide is produced. In this paper, we aim to improve system performance; Cloud Computing based on a decrease in migration of among virtual machines (VM), and reduce energy consumption to be able to manage resources to achieve optimal energy efficiency. For this reason, various techniques such as genetic algorithms (GAs), virtual machine migration and ways Dynamic voltage and frequency scaling (DVFS), and resize virtual machines to reduce energy consumption and fault tolerance are used. The main purpose of this article, the allocation of resources with the aim of reducing energy consumption in cloud computing. The results show that reduced energy consumption and hold down the rate of virtual machines breach of contract, reduces migration as well.
Computer Networks and Distributed Systems
mohammadreza hosseinzadehmoghadam; seyed javad mirabedini; toraj banirostam
Volume 3, Issue 4 , November 2017, , Pages 213-222
Abstract
Abstract— In order to provide complete security in a computer system and to prevent intrusion, intrusion detection systems (IDS) are required to detect if an attacker crosses the firewall, antivirus, and other security devices. Data and options to deal with it. In this paper, we are trying to provide ...
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Abstract— In order to provide complete security in a computer system and to prevent intrusion, intrusion detection systems (IDS) are required to detect if an attacker crosses the firewall, antivirus, and other security devices. Data and options to deal with it. In this paper, we are trying to provide a model for combining types of attacks on public data using combined methods of genetic algorithm and neural network. The goal is to make the designed model act as a measure of system attack and combine optimization algorithms to create the ultimate accuracy and reliability for the proposed model and reduce the error rate. To do this, we used a feedback neural network, and by examining the worker, it can be argued that this research with the new approach reduces errors in the classification.with the rapid development of communication and information technology and its applications, especially in computer networks, there is a new competition in information security and network security.
Pattern Analysis and Intelligent Systems
Zahra Shahpar; Vahid Khatibi; Asma Tanavar; Rahil Sarikhani
Volume 2, Issue 4 , November 2016, , Pages 31-38
Abstract
In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become ...
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In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy.
Hossein Barghi Jond; Adel Akbarimajd; Nurhan Gürsel Özmen; Sonia Gharibzadeh
Volume 2, Issue 3 , August 2016, , Pages 35-42
Abstract
This paper aims to discuss the requirements of safe and smooth trajectory planning of transporter mobile robots to perform non-prehensile object manipulation task. In non-prehensile approach, the robot and the object must keep their grasp-less contact during manipulation task. To this end, dynamic grasp ...
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This paper aims to discuss the requirements of safe and smooth trajectory planning of transporter mobile robots to perform non-prehensile object manipulation task. In non-prehensile approach, the robot and the object must keep their grasp-less contact during manipulation task. To this end, dynamic grasp concept is employed for a box manipulation task and corresponding conditions are obtained and are represented as a bound on robot acceleration. A trajectory optimization problem is defined for general motion where dynamic grasp conditions are regarded as constraint on acceleration. The optimal trajectory planning for linear, circular and curve motions are discussed. Optimization problems for linear and circular trajectories were analytically solved by previous studies and here we focused with curve trajectory where Genetic Algorithm is employed as a solver tool. Motion simulations showed that the resulted trajectories satisfy the acceleration constraint as well as velocity boundary condition that is needed to accomplish non-prehensile box manipulation task.
Pattern Analysis and Intelligent Systems
Rahil Hosseini; Farzaneh Latifi; Mahdi Mazinani
Volume 2, Issue 2 , May 2016, , Pages 33-42
Abstract
Hybrid fuzzy expert systems are one of the most practical intelligent paradigm of soft computing techniques with the high potential for managing uncertainty associated to the medical diagnosis. The potential of genetic algorithm (GA) by inspiring from natural evolution as a learning and optimization ...
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Hybrid fuzzy expert systems are one of the most practical intelligent paradigm of soft computing techniques with the high potential for managing uncertainty associated to the medical diagnosis. The potential of genetic algorithm (GA) by inspiring from natural evolution as a learning and optimization technique has been vastly concentrated for improving fuzzy expert systems. In this paper, the GA capabilities have been applied for optimization of the membership function parameters in a fuzzy inference system (FIS) for diagnosing of acute lymphocytic leukemia in children. The fuzzy expert system utilizes the high interpretability of the Mamdani reasoning model to explain system results to experts in a high level and combines it with the GA optimization capability to improve its performance. The hybrid proposed Fuzzy-GA approach was implemented in Matlab software and evaluated on the real patients’ dataset. High accuracy of this system was achieved after GA tuning process with an accuracy about 98%. The results reveal the hybrid fuzzy-GA approach capability to assist computer-based diagnosis of medical experts, and consequently early diagnosis of the disease which is promising for providing suitable treatment for patients and saving more children’s lives.