Cellular Learning Automata Research Articles

ETFC: Energy-efficient and deadline-aware task scheduling in fog computing

The Internet of Things (IoT) is constantly evolving and expanding. However, due to the limited IoT resources, it is intertwined with fog computing to use their resources to compensate for the limitations of IoT resources. On the other hand, fog devices face challenges, such as resource heterogeneity, high distribution, dynamism, and limitations, so an efficient task scheduling approach is needed to deploy fog computing resources effectively and improve the quality of service (QoS). This work mathematically formulates the task scheduling problem to minimize energy consumption and cost and improve QoS by reducing response time and deadline violation times of IoT tasks. Then, it proposes an Energy-efficient and deadline-Aware Task scheduling in Fog Computing (ETFC) method that predicts the traffic of fog nodes by a Support Vector Machine (SVM) and divides them into low-traffic and high-traffic groups. Next, the ETFC method schedules the low-traffic part with an algorithm based on reinforcement learning using the proposed ICLA-SOA, which is an algorithm based on irregular cellular learning automata and schedules the tasks of the high-traffic part with a metaheuristic algorithm using the proposed Non-dominated Sorting Genetic Algorithm (NSGA-III). The simulation results demonstrate that the ETFC method exhibits up to an 84 % enhancement in response time, up to a 33 % reduction in energy consumption, up to a 30 % decrease in costs, and up to a 28 % advancement in meeting task deadlines compared to other methods.

ELA-RCP: An energy-efficient and load balanced algorithm for reliable controller placement in software-defined networks

Software-defined networking is a modern and popular network technology that utilizes controllers as network operating systems to manage the network and allow user applications to interact with network hardware. Determining the optimal number of controllers and the optimal place to install them are some of the multi-controller model challenges that are known as the Controller Placement Problem (CPP). CPP is an optimization problem that tries to minimize delay and achieve a balanced network. CPP becomes more important when we intend to increase reliability and reduce energy consumption. This paper proposes an Energy-efficient and Load-balanced optimization Algorithm for Reliable Controller Placement (ELA-RCP) in Software-Defined Networks (SDN). The proposed ELA-RCP is a three-step solution to CPPs. First, it determines the optimal number of controllers using the proposed cellular learning automata, taking into account network traffic status, scale, and budget. Second, an innovative meta-heuristic algorithm is proposed to identify proper controller locations based on factors like path reliability, controller processing capacity, propagation delay, and energy consumption. In the last step, load balancing and the reliability of the controllers are improved using queuing theory and a proposed greedy algorithm. ELA-RCP is evaluated with real-world topologies. The simulation demonstrates up to 25% improvement in energy consumption, up to 108% improvement in the network survivability, and up to 68% improvement in mean average delay rise compared to particle swarm optimization, firefly optimization algorithm, ant lion optimization, and seagull optimization algorithm.

The improvement of wavefront cellular learning automata for task scheduling in fog computing

AbstractThe rapid advancement of the “Internet of Things” (IoT) devices has led to the emergence of different types of IoT applications that need immediate response and low delay to operate. The emergence of fog computing has provided a proper platform to process fast‐emerging IoT applications. Nevertheless, to name the disadvantages of fog computing devices, it can be said that they are typically distributed, dynamic, and resource‐limited. Therefore, it seems a substantial challenge to schedule fog computational resources effectively to perform heterogeneous and delay‐sensitive IoT tasks. The problem of scheduling tasks aimed at minimizing the energy consumption of fog nodes is formulated in this article, while meeting the requirements of the quality of service (QoS) of IoT tasks, including response time. Minimizing the deadline time and balancing the network load are also considered in the mathematical model. In the next stage, a new algorithm is introduced based on a wavefront cellular learning automata (WCLA) called the wavefront cellular learning automata improved by genetic algorithm (WCLA + GA). WCLA + GA is indeed a modified version of WCLA that has been improved using the genetic algorithm. In this version, the WCLA reinforcement signal is regulated by a genetic algorithm that accelerates the automata convergence rate. WCLA + GA is then utilized to schedule fog tasks. Simulating the proposed method followed by comparing it with other methods demonstrates that WCLA + GA performs task scheduling significantly better in terms of response time, energy consumption, and percentage of tasks that meet their deadline.

TRCLA: A Transfer Learning Approach to Reduce Negative Transfer for Cellular Learning Automata.

In most traditional machine learning algorithms, the training and testing datasets have identical distributions and feature spaces. However, these assumptions have not held in many real applications. Although transfer learning methods have been invented to fill this gap, they introduce new challenges as negative transfers (NTs). Most previous research considered NT a significant problem, but they pay less attention to solving it. This study will propose a transductive learning algorithm based on cellular learning automata (CLA) to alleviate the NT issue. Two famous learning automata (LA) entitled estimators are applied as estimator CLA in the proposed algorithms. A couple of new decision criteria called merit and and attitude parameters are introduced to CLA to limit NT. The proposed algorithms are applied to standard LA environments. The experiments show that the proposed algorithm leads to higher accuracy and less NT results.

New Cellular Learning Automata as a framework for online link prediction problem

ABSTRACT One of the main areas of research in Social Network Analysis (SNA) is Link Prediction (LP). The LP problem is useful in understanding the evolution mechanism of social networks, as well as in different applications such as recommendation systems, bioinformatics and marketing. In LP algorithms, prior network information is used to predict future connections in social networks. In this paper, we introduce a multi-wave cellular learning automaton (MWCLA) and use it to solve the LP problem in social networks. This model is a new CLA with a connected structure and a module of LAs in each cell where the neighbours of the cell module are its successors. The MWCLA method uses multiple waves at the same time in the network in order to improve convergence speed as well as accuracy. For predicting links in the social network, multiple waves can be used to consider different aspects of the network. Here we show that the model converges upon a stable and compatible configuration. Compared to some state-of-the-art approaches, MWCLA produces significantly better results when applied to the LP problem.

PARALLEL FUZZY FREQUENT ITEMSET MINING USING CELLULAR AUTOMATA

Finding frequent fuzzy itemsets in operational quantitative databases is a significant challenge for fuzzy association rule mining in the context of data mining. If frequent fuzzy itemsets are detected, the decision-making process and formulating strategies in businesses will be made more precise. Because the characteristic of these data models is a large number of transactions and unlimited and high-speed productions. This leads to limitations in calculating the support for itemsets containing fuzzy attributes. As a result, mining using parallel processing techniques has emerged as a potential solution to the issue of slow availability. This study presents a reinforced technique for mining frequent fuzzy sets based on cellular learning automata (CLA). The results demonstrate that frequent set mining can be accomplished with less running time when the proposed method is compared to iMFFP and NPSFF methods.

A new multi-wave continuous action-set cellular learning automata for link prediction problem in weighted multi-layer social networks

A new multi-wave continuous action-set cellular learning automata for link prediction problem in weighted multi-layer social networks

Multilayer cellular learning automata: A computational model to solve multilayer infrastructure problems with its application in community detection for multilayer networks

Multilayer networks are one type of complex systems that have multiple types of interactions among their nodes. Through the development and extension of the real-world networks, especially social and biological networks, understanding the structure and organization of multilayer systems become more significant. In this paper, we propose a new computational model based on learning automata to overcome the problems related to multilayer networks. In our proposed model, the whole network is modeled as a set of layers in which an irregular cellular learning automata (ICLA) is assigned to each node in a layer. The functionality of cellular learning automata (CLA) is dependent on two important features: local and global environments. In this model, the information from the other layers is evaluated as the global environments, and update rules are performed based on the information from the other layers. Finally, by the convergence of CLA in each layer the global environments cause the multilayer cellular learning automata (MLCLA) to cover all possible states in all nodes of layers. We apply this model to community detection in multilayer networks as an example of the application of the proposed model. To solve this, each CLA by the depth-first search algorithm detects the optimal community in a layer and then sends the information about the identified community to the next layer as a feature vector. The simulation results on multiple well-known multilayer datasets demonstrate the effectiveness and superiority of the proposed algorithm in terms of similarity and modularity measures, and the analysis results of the proposed algorithm in terms of Precision, Recall, and F1-score are higher than the other available community detection methods.

A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory

A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory

RALF: an adaptive reinforcement learning framework for teaching dyslexic students.

Dyslexia is a learning disorder in which individuals have significant reading difficulties. Previous studies found that using machine learning techniques in content supplements is vital in adapting the course concepts to the learners' educational level. However, to the best of our knowledge, no research objectively applied machine learning methods to adaptive content generation. This study introduces an adaptive reinforcement learning framework known as RALF through Cellular Learning Automata (CLA) to generate content automatically for students with dyslexia. At first, RALF generates online alphabet models as a simplified font. CLA structure learns each rule of character generation through the reinforcement learning cycle asynchronously. Second, Persian words are generated algorithmically. This process also considers each character's state to decide the alphabet cursiveness and the cells' response to the environment. Finally, RALF can generate long texts and sentences using the embedded word-formation algorithm. The spaces between words are proceeds through the CLA neighboring states. Besides, RALF provides word pronunciation and several exams and games to improve the learning performance of people with dyslexia. The proposed reinforcement learning tool enhances students' learning rate with dyslexia by almost 27% compared to the face-to-face approach. The findings of this research show the applicability of this approach in dyslexia treatment during Lockdown of COVID-19.

Automated classification of African embroidery patterns using cellular learning automata and support vector machines

Embroidery is the art that is majorly practised in Nigeria, which requires creativity and skills. However, differentiating between two standard embroidery patterns pose challenges to wearers of the patterns. This study developed a classification system to improve the embroiderer to user relationship. The specific characteristics are used as feature sets to classify two common African embroidery patterns (handmade and tinko) are shape, brightness, thickness and colour. The system developed and simulated in MATLAB 2016a environment employed Cellular Learning Automata (CLA) and Support Vector Machine (SVM) as its classifier. The classification performance of the proposed system was evaluated using precision, recall, and accuracy. The system obtained an average precision of 0.93, average recall of 0.81, and average accuracy of 0.97 in classifying the handmade and tinko embroidery patterns considered in this study. This study also presented an experimental result of three validation models for training and testing the dataset used in this study. The model developed an improved and refined embroiderer for eliminating stress related to the manual pattern identification process.

A new method to detect attacks on the Internet of Things (IoT) using adaptive learning based on cellular learning automata

The Internet of Things (IoT) is a new paradigm that connects physical and virtual objects from various domains such as home automation, industrial processes, human health, and monitoring. IoT sensors receive information from their environment and forward it to their neighboring nodes. However, the large amounts of exchanged data are vulnerable to attacks that reduce the network performance. Most of the previous security methods for IoT have neglected the energy consumption of IoT, thereby affecting the performance and reducing the network lifetime. This paper presents a new multistep routing protocol based on cellular learning automata. The network lifetime is improved by a performance-based adaptive reward and fine parameters. Nodes can vote on the reliability of their neighbors, achieving network reliability and a reasonable level of security. Overall, the proposed method balances the security and reliability with the energy consumption of the network.

Reliable controller placement in software‐defined networks using reliability covering graph and irregular cellular learning automata

SummarySoftware‐defined networking is a new paradigm in network management that has received much attention in recent years. One of the important challenges in designing software‐defined networks (SDNs) is the reliable controller placement problem (RCPP) that is the problem of finding the number and locations of controllers to satisfy the reliability requirements. The high complexity of exact reliability measurement led researchers to use estimation techniques that results in overestimating the number of controllers and incorrect placement. In this paper, we introduce a new concept named reliability covering graph (RCG) that enables us to use the exact reliability measurement (ERM) methods in solving the RCPP with desired level of complexity. To construct the RCG, for each candidate node, a subgraph with the adjustable radiusris extracted from the network graph. In the next step, the RCG is developed by calculating the exact reliability between the central node and any other nodes in the resulting subgraphs. In the last step, the RCG is converted to a machine learning tool named irregular cellular learning automata (ICLA) that determines the number and locations of controllers as well as the switches they cover. To study the efficiency of the proposed method, its performance is compared with the optimal results obtained from the ILP solver. The simulation results of 800 case studies on 100 standard topologies show that the average difference in results is 0.00125. Thanks to our innovative structure RCG, the number of controllers required is significantly reduced compared to previous methods.

Clustering-based hybrid feature selection approach for high dimensional microarray data

The DNA microarrays are used to monitor the expression levels of significant genes. Most of the microarray data are assumed to be high dimensional, redundant, and noisy. This paper proposed a clustering-based hybrid gene selection approach to reduce the high dimensionality and increase the classification accuracy of cancer microarray data. The proposed approach uses the combined method of k-means clustering algorithm and signal-to-noise-ratio ranking method as a primary filtering method to reduce the high dimensionality of the microarray dataset. A cellular learning automaton combined with ant colony optimization is then applied on the reduced dataset as a wrapper method to get the optimized gene subset. The classifiers adopted to evaluate the proposed method are support vector machine, K-nearest neighbor, and Naive Bayes. The experiments showed promising results in gene subset selection and classification.

A variable action set cellular learning automata-based algorithm for link prediction in online social networks

Link prediction (LP) is a crucial issue in the online social network (OSN) evolution analysis. Since OSNs are growing in size on a daily basis, a growing need for scalable LP algorithms is being felt. OSNs are innately evolutionary, such that the characteristics, behavior, and activities of their components (including nodes and links) change over time. In analyzing social networks which are based on the time evolution model, LP helps us realize the logic of social network growth. Deriving time patterns of evolutionary changes according to the communities and neighbors of nodes in a network can be aptly used for LP. This article introduces a new algorithm based on irregular cellular learning automata (ICLAs) for LP in the near future in OSNs. The algorithm we propose here models the network as an ICLA. The ICLA weighs the real links in the network according to entities' participation in forming communities over consecutive time periods. This method lies in the premise that social networks include communities. Based on the communities formed over successive time periods, the presented method calculates the probability of link formation between every pair of nodes which are unconnected at the present time, estimating the chances of their connection in the near future. Experiments performed on real social networks show that the proposed algorithm produces good results in predicting link formation in OSNs.

A new irregular cellular learning automata-based evolutionary computation for time series link prediction in social networks

Link prediction (LP), as an attempt to predict event-based future connections within a network, is the main task of social network analysis (SNA). Accordingly, common LP approaches to forecast future connections utilize similarity metrics of non-connected links in a static network representation. A general shortcoming of most existing research studies in this field is that they tap the present condition of a system and fail to take any temporal events into account. Moreover; social networks are innately evolutionary since they are assumed to be online, non-deterministic, and unforeseeable in most applications. Consequently, it is not appropriate to employ deterministic models for examining actual social network problems. With regard to time-series LP (TSLP) problems, temporal evolution of connection incidence is correspondingly exploited to predict connection chances at a particular time. In this paper, a new TSLP method based on irregular cellular learning automaton (ICLA) and evolutionary computation (EC) is proposed. In the evolutionary procedure suggested here, each vertex (i.e. cell) includes a genome as well as a set of learning automata (LAs). Accordingly, the genome residing in a cell represents predicted links for the corresponding cell. Local information among cells in successive time 1 to T in the network is then analyzed to predict future connections in time T + 1. According to the distributed feature of the recommended approach, each genome is locally developed by a local search. The experiments in this study via e-mail and co-authorship networks ultimately show that the proposed algorithm leads to remarkable outcomes in predicting future connections.

An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications

AbstractNowadays, increase in time complexity of applications and decrease in hardware costs are two major contributing drivers for the utilization of high‐performance architectures such as cluster computing systems. Actually, cluster computing environments, in the contemporary sophisticated data centres, provide the main infrastructure to process various data, where the biomedical one is not an exception. Indeed, optimized task scheduling is key to achieve high performance in such computing environments. The most distractive assumption about the problem of task scheduling, made by the state‐of‐the‐art approaches, is to assume the problem as a whole and try to enhance the overall performance, while the problem is actually consisted of two disparate‐in‐nature subproblems, that is, sequencing subproblem and assigning one, each of which needs some special considerations. In this paper, an efficient hybrid approach named ACO‐CLA is proposed to solve task scheduling problem in the mesh‐topology cluster computing environments. In the proposed approach, an enhanced ant colony optimization (ACO) is developed to solve the sequence subproblem, whereas a cellular learning automata (CLA) machine tackles the assigning subproblem. The utilization of background knowledge about the problem (i.e., tasks' priorities) has made the proposed approach very robust and efficient. A randomly generated data set consisting of 125 different random task graphs with various shape parameters, like the ones frequently encountered in the biomedicine, has been utilized for the evaluation of the proposed approach. The conducted comparison study clearly shows the efficiency and superiority of the proposed approach versus traditional counterparts in terms of the performance. From our first metric, that is, the NSL (normalized schedule length) point of view, the proposed ACO‐CLA is 2.48% and 5.55% better than the ETF (earliest time first), which is the second‐best approach, and the average performance of all other competing methods. On the other hand, from our second metric, that is, the speedup perspective, the proposed ACO‐CLA is 2.66% and 5.15% better than the ETF (the second‐best approach) and the average performance of all the other competitors.

Classification of Normal and Abnormal Lung CT-scan Images Using Cellular Learning Automata

This paper proposes a medical pattern recognition system based on the Cellular automata (CA). CA or cellular machine is a dynamic mathematical model that consists of several similar and simple units organized by considerably simple local rules. Each cell acts as a simple computer automaton. This can lead to the implementation of the complex computations through uncomplicated methods. However, the CA model needs to determine certain rules for specific use and this model is regarded as suitable for modelling certain systems. To overcome this problem, a method is needed through which the favorable rules are extracted. Cellular Learning Automata (CLA) model is obtained from developing CA by appending a Learning Automaton (LA) to each cell. Many applications of CA are known today, especially in the field of pattern recognition. Therefore in this study, we use the CLA to design an automatic system to diagnosis the images which contain cancer tissue. Hence in this study, after applying the required approaches on lung Computed Tomography (CT) images, images are classified through the CLA model and the proposed methods are evaluated in terms of sensitivity, specificity and accuracy. The proposed system promises a flexible and low complexity model. The method has been tested on 22 slices of CT scan images from a real-world dataset and has yielded satisfactory results. The model with a low error rate (0.09), yielded a favorable accuracy (95.4%).

Multiple sclerosis Lesion Detection via Machine Learning Algorithm based on converting 3D to 2D MRI images

In the twenty first century, there have been various scientific discoveries which have helped in addressing some of the fundamental health issues. Specifically, the discovery of machines which are able to assess the internal conditions of individuals has been a significant boost in the medical field. This paper or case study is the continuation of a previous research which aimed to create artificial models using support vector machines (SVM) to classify MS and normal brain MRI images, analyze the effectiveness of these models and their potential to use them in Multiple Sclerosis (MS) diagnosis. In the previous study presented at the Cognitive InfoCommunication (CogInfoCom 2019) conference, we intend to show that 3D images can be converted into 2D and by considering machine learning techniques and SVM tools. The previous paper concluded that SVM is a potential method which can be involved during MS diagnosis, however, in order to confirm this statement more research and other potentially effective methods should be included in the research and need to be tested. First, this study continues the research of SVM used for classification and Cellular Learning Automata (CLA), then it expands the research to other method such as Artificial Neural Networks (ANN) and k-Nearest Neighbor (k-NN) and then compares the results of these.

Ripplet-Transform-based Cycle Spinning Denoising and Fuzzy-CLA Segmentation of Retinal Images for Accurate Hard Exudates and Lesion Detection

Aims: Digital retinal images are commonly used for hard exudates and lesion detection. These images are rarely noiseless and therefore before any further processing they should be underwent noise removal. Background: An efficient segmentation method is then needed to detect and discern the lesions from the retinal area. Objective: In this paper, a hybrid method is presented for digital retinal image processing for diagnosis and screening purposes. The aim of this study is to present a supervised/semi-supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure. Methods: Ripplet transform and cycle spinning method is first used to remove the noises and artifacts. Results: The noises may be normal or any other commonly occurring forms such as salt and pepper. The image is transformed into fuzzy domain after it is denoised. Conclusion: A cellular learning automata model is used to detect any abnormality on the image which is related to a lesion. The automaton is created with an extra term as the rule updating term to improve the adaptability and efficiency of the cellular automata.Three main statistical criteria are introduced as the sensitivity, specificity and accuracy. A number of 50 retinal images with visually detection hard exudates and lesions are the experimental dataset for evaluation and validation of the method.