Exploiting Computational Intelligence Paradigms in e-Technologies ...

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ScienceDirect Procedia Computer Science 65 (2015) 396 – 405

International Conference on Communication, Management and Information Technology (ICCMIT 2015)

Exploiting Computational intelligence Paradigms in e- Technologies and Activities Hanaa M. Said and Abdel-Badeeh M. Salem Faculty of Computer and Information Sciences- Ain Shams University Cairo, Egypt [email protected], [email protected]

Abstract Computational intelligence (CI) has emerged as a powerful paradigm in e-Science, providing the researchers an immense volume of intelligent computing techniques and algorithms.CI provides knowledge engineers to develop a robust techniques and intelligent tools for e-government applications and tasks. This paper presents a comparative analysis of some techniques used in e-activities and e-government systems. The study includes the following paradigms; artificial neural networks, fuzzy logic, genetic algorithms, case-based reasoning, support vector machines, and swarm intelligence. Additionally, this study found that such paradigms offer many business benefits and advantages ;e.g. (a)the ability to acquire, represent, manage and structure the knowledge in the domain under study,(b) the ability to optimize resources,(c ) the ability to perform efficient performance , and (d) the ability to conduct planning, budgeting, and forecasting . © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-reviewunder underresponsibility responsibility of Universal Society for Applied Research. Peer-review of Universal Society for Applied Research Keywords: Machine Learning, Intelligent Data Analysis, E- Technologies government, Neural Networks, Fuzzy Logic, Genetic algorithm, Case based reasoning, SVM, Swarm intelligence, computational intelligence;

1. Introduction Artificial intelligence (AI) is a subdivision of computer science devoted to creating computer software and hardware that initiates the human mind. The main goal of AI is to make the computer smarter by creating software that will

1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Universal Society for Applied Research doi:10.1016/j.procs.2015.09.101

Hanaa M. Said and Abdel-Badeeh M. Salem / Procedia Computer Science 65 (2015) 396 – 405

allow a computer to mimic some of the functions of the human brain in selected application. The idea is not to replace human beings, but to provide us a more powerful tool to assist in our work. AI give computer added computing capabilities, allowing them to exhibit more intelligent behavior. Intelligence, the ability of human being to acquire knowledge and apply it, means the capability of thinking and reasoning. To a limited degree, artificial intelligence permit computers to accept knowledge from human, then use that knowledge through simulated thoughts and reasoning process to solve problems. The aim of this paper is to discuss the application of computational intelligence (CI) in E- Technologies government tasks and domains. The following E- Technologies government; e- technologies Processing, Multimedia Systems, EBusiness, e-health, e-learning, e-economy , e-commerce, e-society applications, and medical applications are discussed from computational intelligence (CI) prospective [4]. On the other side, computational intelligence (CI) is a sub-branch of AI. This computing branch is concerned with the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environment. These mechanisms include those AI paradigms that exhibit an ability to learn or to adapt to new situations, to generalize, abstract, discover, and associate. This computing area includes the following paradigms: artificial neural networks (ANN), fuzzy systems (FS), rough sets (RS), genetic algorithms (GA), evolutionary computing (EC), support vector machines (SVM), and swarm intelligence (SI).Each of the CI paradigms has its origins in biological systems. ANNs model biological neural systems, EC models natural evolution (including genetic and behavioral evolution), SI models the social behavior organisms living in swarms or colonies, and FS originated from studies of how organisms interact with their environment and human thinking processes. On the other hand, soft computing, a term coined by, Zadeh, is a different grouping of paradigms, which usually refers to the collective set of CI paradigms and probabilistic methods. This study discusses the exploiting some of the computational intelligence paradigms in the applications of e-technologies and activities, e.g. e-business, e-health, e-learning, e-economy, e-commerce, e-court. The remaining of the paper is organized as follows: in section 2 some of the well known computational intelligence paradigms are discussed. Analysis and discussion are presented in Section 3, while Section 4 concludes this paper.

2. Computational Intelligence Paradigms In this section, we discuses in brief the technical aspects of five of the computational intelligence paradigms used in our study, namely; ANN, FS, GA, RS, and SVM. 2.1. Artificial Neural Networks (ANN) ANN are inspired in biological models of brain functioning. They are capable of learning by examples and generalizing the acquired knowledge. Due to these abilities the neural networks are widely used to find out nonlinear relations which otherwise could not be unveiled due to analytical constraints. The learned knowledge is hidden in their structure thus it is not possibly to be easily extracted and interpreted. The structure of the multilayered perception, i.e. the number of hidden layers and the number of neurons, determines its capacity, while the knowledge about the relations between input and output data is stored in the weights of connections between neurons. The values of weights are updated in the supervised training process with a set of known and representative values of input – output data samples. ANN learning methods provides a robust approach to approximating real-valued, discrete valued and robust functions. For certain types of problems, such as learning to interpret complex real-world sensor data, artificial neural networks are the most effective learning methods currently known [54]. 2.2. Support Vector Machines(SVM) SVM are new learning-by example paradigm for classification and regression problems [5]. SVM have demonstrated significant efficiency when compared with neural networks. Their main advantage lies in the structure of the learning algorithm which consists of a constrained quadratic optimization problem (QP), thus avoiding the local minima drawback of neural networks (NN). The approach has its roots in statistical learning theory (SLT) and provides a way to build “optimum classifiers” according to some optimality criterion that is referred to as the maximal margin

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criterion. An interesting development in SLT is the introduction of the Vapnik- Chervonenkis (VC) dimension, which is a measure of the complexity of the model. Equipped with a sound mathematical background, support vector machines treat both the problem of how to minimize complexity in the course of learning and how high generalization might be attained. This trade-off between complexity and accuracy led to a range of principles to find the optimal compromise. Vapnik and co-authors' work have shown the generalization to be bounded by the sum of the training error and a term depending on the Vapnik- Chervonenkis (VC) dimension of the learning machine leading to the formulation of the structural risk minimization (SRM) principle. By minimizing this upper bound, which typically depends on the margin of the classifier, the resulting algorithms lead to high generalization in the learning process.

2.3.

Genetic Algorithms (GA)

Many classifications models have been proposed in the literature, such as distributed algorithms, restricted search, data reduction algorithms, parallel algorithms, neural networks and decision trees, genetic algorithms. These approaches either cause loss of accuracy or cannot effectively uncover the data structure. Genetic Algorithms (GA) provide an approach to learning that based loosely on simulated evolution. The GA methodology hinges on a population of potential solutions, and as such exploits the mechanisms of natural selection well known in evolution. Rather than searching from general to specific hypothesis or from simple to complex GA generates successive hypotheses by repeatedly mutating and recombining parts of the best currently known hypotheses. The GA algorithm operates by iteratively updating a poll of hypotheses (population). One each iteration, old members of the population are evaluated according a fitness function. A new generation is then generated by probabilistically selecting the fittest individuals from the current population. Some of these selected individuals are carried forward into the next generation population others are used as the bases for creating new offspring individuals by applying genetic operations such as crossover and mutation [33]. 2.4. Rough sets(RS) Rough set theory was proposed as a new approach to vague concept description from incomplete data. The rough set theory is one of the most useful techniques in many real life applications such as medicine, pharmacology, engineering, banking and market analysis. This theory provides a powerful foundation to reveal and discover important structures in data and to classify complex objects. One of the main advantages of rough set theory is that it does not need any preliminary or additional information about data [44]. 2.5. Fuzzy Systems(FS) The term "fuzzy logic" was introduced with the 1965 proposal of fuzzy set theory by Lotfi A. Zadeh. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. Zadeh explained that Fuzzy logic [9] is an extension of Boolean logic that is often used for computer-based complex decision making. While in classical Boolean logic an element can be either a full member or non-member of a Boolean (sometimes called ”crisp”) set, the membership of an element to a fuzzy set can be any value within the interval (0, 1), allowing also partial membership of an element in a set [26,27]. A fuzzy e-system consists of three different types of entities: fuzzy sets, fuzzy variables and fuzzy rules. The membership of a fuzzy variable in a fuzzy set is determined by a function that produces values within the interval (0, 1). These functions are called membership functions. Fuzzy variables are divided into two groups: antecedent variables, that are assigned with the input data of the fuzzy expert system and consequent variables, that are assigned with the results computed by the system. The process of a fuzzy system has three steps. These steps are Fuzzification, Rule Evaluation, and Defuzzification. In the fuzzification step, the input crisp values are transformed into degrees of

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membership in the fuzzy sets. The degree of membership of each crisp value in each fuzzy set is determined by plugging the value into the membership function associated with the fuzzy set. In the rule evaluation step, each fuzzy rule is assigned with a strength value. The strength is determined by the degrees of memberships of the crisp input values in the fuzzy sets of antecedent part of the fuzzy rule. The defuzzification stage transposes the fuzzy outputs into crisp values. [20].

3. Analysis and Discussion During the last five years, there are a growing number of specialized publications and research projects that pay greater attention to the e-activities and technologies. Based on our scientific analysis of the published results during the last five years, tables 1, 2, 3, 4,5and 6 show the different roles of the CI paradigms in e-activities for different applications. Our analysis includes the following techniques; artificial neural networks, fuzzy logic, genetic algorithm, Case based reasoning, support vector machines, and swarm intelligence. Table 1. Artificial neural networks paradigm in e- technologies Application

Knowledge Types and Tasks

e-Business [14]

Developing Knowledge Bases Classification , Clustering Early Warning and Proactive, Control Systems.

e-Business [16]

Classification Early Warning and Proactive Control Systems

e-health

is used to design neural network With joint weight. In this algorithm testing of neural network controller is done by classic boost power transformer

e-economy [18]

For clustering of large database Using the exemplar. Power system analysis computing, Power system measurements.

e-commerce [19]

data mining classification and clustering tasks

e-health [20]

medical data mining .

e-economy [21] Analysis of web mining applications. e-commerce [22]

Mining the data over the www using various data through.E-government systems

Table 2. Fuzzy logic paradigm in e- technologies Application

Knowledge Types and Tasks

e-Business [23]

Assignment of genes to templates is based on fuzzy membership function. Multi-objective evolutionary algorithm is used to determine compact clusters with varying number of templates.

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e-Business [24]

Historical flood data are clustered into several partitions by applying fuzzy clustering iteration algorithm and the optimal number of cluster is obtained by using cluster validity criterion, and a real-time flood Hydrograph partition is recognized by fuzzy pattern recognition model.

e-market [25]

Knowledge Discovery

e – health [26]

Analysis of transformation of the probed data to a linguistic level using fuzzy set theory

e-Business [27]

Focusing on rule-based fuzzy controllers. It is argued that the dominant position of analytic control theory prevented fuzzy control from being taken seriously until its increasing application in Japan

e-Market [28]

Knowledge Discovery using fuzzy logic

E-Learning [29]

Fuzzy Intrusion Detection

E-Commerce [30]

Analysis of web mining applications.

Table 3. Genetic algorithm (GA) techniques in e- technologies applications

Knowledge Types and tasks

E-Learning [31]

The application of GA techniques to the problem of overloaded arrays, in which the number of transmitted narrowband signals is greater than the number of receiver array elements, is explored.

E-Business [32]

The difference is defined using the fuzzy systems contours of each image. The search of the optimal translation is considered as a minimization process.

E-Business [33]

A stabilizing compensator designed using genetic optimization combined with Hinfin design. The proposed controller can be implemented digitally with synchronous sampling.

E-Business [34]

A method is proposed to automatically extract numerical control rules from the sensor data without the help of experts by means of a GA.

E-Business [35]

It adopts GA to give information pheromone to distribute. And, it makes use of the ant colony algorithm to get several solutions through information pheromone accumulation and renewal.

E-Business [36]

GA is added to ant colony algorithms every generation in the proposed algorithm. Making use of GAs advantage of whole quick convergence, ant colony algorithm's convergence speed is quickened.

E-Business [37]

this paper analyzes the characteristics and shortcomings of simple GA, simulated annealing genetic algorithm as well as immune algorithm respectively

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Table 4. Case based reasoning paradigm in e- technologies Application

Knowledge Types and tasks

e-Business [38]

Ontology-based distributed case-based reasoning to effectively support knowledge retrieval within the virtual enterprise environment.

e-Business [39]

proposed a concept of software realization of a hybrid expert system

e-Business [40]

Induction learning technique.

e-Business[44]

Retrieval mechanism of case-based reasoning in fixture design is analyzed.

e-Commerce[41]

To identify the similarity between the cases and to develop single and consistent rule set with their help.

e-Commerce [42]

Operates on a software process that includes various sub-processes and utilises past experience by CBR technique to increase its effectiveness.

e-Business [43]

Creation of customized management model by means of CBR technique to satisfy the specific management requirements.

e-Business [44]

Solving complex case adaptation problems.

E- tourism [55]

Uses CBR techniques in the management and organizing tasks of the tourism processes and activities.

Table 5. Support vector machines paradigm in e- technologies Applications

Knowledge Types and tasks

E-Business [45]

Support Vector Machines are trained on large-scale datasets and SVM classifying accuracy

E-government information system [46]

A classification algorithm for E-government document based on support vector machine is proposed.

E-learning [47]

The SVM classifier is approved to detect unknown samples of malware with the probability of 74 - 83 percent

E-Business [48]

The individual components then can be classified as bio- or nonbioaerosol by our SVM classifier.

E-Commerce [49]

To develop innovative services to cope with customers' evolving demands and to create customers' value

E-Business [50]

For the general workflow of E-government document circulation in the current information system, a scheme of the auto-

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classification and archiving based on service is presented so that it is realized in a heterogeneous information system E-Business [51]

The experiment results show that the new parallel SVM training algorithm is efficient and the SVM classifying

E – Bank [52]

This study applies Support Vector Machines (SVM) and Artificial Neural Network (ANN) as methods for determining the visual condition of roads on an inventory and traffic data set

E-Commerce [53]

An experiment based on the SVM model shows that the proposed method can detect malware with strong resilience and high accuracy

Table 6. Swarm intelligence paradigm in e- technologies Applications

Knowledge Types and Tasks

E-Business [54]

focus on hierarchical task network

e-mail policy [56]

Their initiatives for implementing business-grade electronic mail (email) services for federal, state, local, and tribal agencies.

e- Learning [57]

Provide a quick sorting speed and reliable evaluation with a long lifespan, radio frequency identification (RFID) system is often employed in practice.

E-Business [58]

Regression Test suite optimization is an effective technique to reduce time and cost of testing.

E-Business [59]

This research with traditional AI approaches and focus on hierarchical task network (HTN) descriptions of constraints

E-Commerce [60]

this paper we look forward into identifying suspicious behaviors in egovernment procurement systems, through the use of business intelligence techniques

From the above tables, it can be seen the following observations; x Artificial neural networks paradigm have been used for a wide range of applications, including classification, clustering, medical diagnosis, control and power systems, and others. x Support vector machines and swarm intelligence paradigms have been applied to many e- economic activities, producing state-of-the-art results. x Fuzzy-logic and genetic algorithms paradigms have been applied successfully to e-business, e-market, and ecommerce applications.

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x CBR paradigm has been used successfully in e-business and e-commerce for the following tasks, knowledge retrieval, and software realization of hybrid expert systems, induction learning technique, management requirements, and solving complex case adaptation problems. x Ontologies were developed in e-systems to facilitate knowledge sharing, refine, search and reuse. x CI assists in identifying patterns of criminal or terrorist behavior, identifying emerging criminal or terror threats, and predicting future criminal or terrorist actions. However, in practice, there is no Computational intelligence (CI) technique with 100% accuracy, but it is possible to use assistant analysis in identification of the most accurate and acceptable Computational intelligence (CI) technique. 4. Conclusions

Computational intelligence paradigms provide an effective computational techniques and robust environment for all decision making tasks of e-activities and technologies. CI paradigms offer intelligent computational methods for accumulating, changing and updating knowledge in e- systems, and in particular learning mechanisms that will help us to induce knowledge from information or data .Using such paradigms is well-motivated and has many business benefits and advantages; (a)the ability to acquire, represent, manage and structure the knowledge in the domain under study,(b) the ability to optimize resources,(c ) the ability to perform efficient performance , and (d) the ability to conduct planning, budgeting, and forecasting . The variety of computational intelligence paradigms/techniques/algorithms enabling the design of robust egovernment applications and systems, the key to the success of such systems is the appropriate selection of the CI paradigm that best fits the domain knowledge and the problem to be solved. That choice is depends on the experience of the knowledge engineer. In the future, it is expected that there will be several challenges related to operation and development of CI-based e-systems, especially if the development will take into consideration the dynamic and automated aspects. Overall, it is hoped that this study will provide motivation for further research using CI for escience and technologies. References 1. 2.

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