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IAES International Journal of Artificial Intelligence (IJ-AI)
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IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Articles
219
Articles
Academic performance prediction algorithm based on fuzzy data mining

Kumar Tiwari, Anil, Ramakrishna, G., Kumar Sharma, Lokesh, Kumar Kashyap, Sunil

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

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Abstract

This paper presents an algorithm for prediction of academic performance of students by fuzzy data mining. The fuzzy-trace concept applied to predict the academic performance of the students. An algorithm is proposed in this paper lies with this idea. The fuzzy academic set is generated from the student’s academic data. This is analyzed by the fuzzy-matrix set. The prediction

Optimization study of fuzzy parametric uncertain system

D. Apale, Tejal, B. Patil, Ajay

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

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Abstract

This paper deals with the analysis and design of the optimal robust controller for the fuzzy parametric uncertain system. An LTI system in which coefficients depends on parameters described by a fuzzy function is called as fuzzy parametric uncertain system. By optimal control design, we get control law and feedback gain matrix which can stabilize the system. The robust controller design is a difficult task so we go for the optimal control approach. The system can be converted into state space controllable canonical form with the α-cut property fuzzy. For optimal control design, we find control law and get the feedback gain matrix which can stabilize the system and optimizes the cost function. Stability analysis is done by using the Kharitonov theorem and Lyapunov-Popov method. The proposed method applied to a response of Continuous Stirred Tank Reactor (CSTR).

Multilayer neural network synchronized secured session key based encryption in wireless communication

Sarkar, Arindam

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, multilayer neural network synchronized session key based encryption has been proposed for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights or hidden units of this selected hidden layer help to form a secret session key. The plain text is encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the final block of plain  text is less than the size of the key then this block is kept unaltered.  Receiver will use identical multilayer perceptron generated session key for performing deciphering process for getting the plain text. Parametric tests have been  done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed technique.

Comparative between (LiNbO3) and (LiTaO3) in detecting acoustics microwaves using classification

Hichem, Hafdaoui, Djamel, Benatia

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

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Abstract

Our work is mainly about detecting acoustics microwaves in the type of BAW (Bulk acoustic waves), where we compared between Lithium Niobate (LiNbO3) and Lithium Tantalate (LiTaO3) ,during the propagation of acoustic microwaves in a piezoelectric substrate. In this paper, We have used the classification by Probabilistic Neural Network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity for conclude whichever is the best in utilization for generating bulk acoustic waves.This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

An enhanced hybridized artificial bee colony algorithm for optimization problems

Huang, Xingwang, Zeng, Xuewen, Han, Rui, Wang, Xu

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

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Abstract

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

Dwindling of Real Power Loss by Enriched Big Bang-Big Crunch Algorithm

Lenin, K.

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, Enriched Big Bang-Big Crunch (EBC) algorithm is proposed to solve the reactive power problem. The problem of converging to local optimum solutions occurred for the Bang-Big Crunch (BB-BC) approach due to greedily looking around the best ever found solutions. The proposed algorithm takes advantages of typical Big Bang-Big Crunch (BB-BC) algorithm and enhances it with the proper balance between exploration and exploitation factors. Proposed EBC algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the improved performance of the proposed algorithm in reducing the real power loss.

Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor

Al_Duais, Mohammed Sarhan, Mohamad, Fatma Susilawati.

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Original Source | Check in Google Scholar | Full PDF (497.791 KB)

Abstract

The man problem of batch back propagation (BBP) algorithm is slow training and there are several parameters needs to be adjusted manually, also suffers from saturation training.The learning rate and momentum factor are significant parameters for increasing the efficiency of the (BBP). In this study, we created a new dynamic function of each learning rate and momentum facor. We present the DBBPLM algorithm, which trains with a dynamic function for each the learning rate and momentum factor. A Sigmoid function used as activation function. The XOR problem, balance, breast cancer and iris dataset were used as benchmarks for testing the effects of the dynamic DBBPLM algorithm. All the experiments were performed on Matlab 2012 a. The stop training was determined ten power -5. From the experimental results, the DBBPLM algorithm provides superior performance in terms of training, and faster training with higher accuracy compared to the BBP algorithm and with existing works.

A Novel Optimization Algorithm Based on Stinging Behavior of Bee

Jayalakshmi, S., Aswini, R.

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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Abstract

Optimization algorithms are search methods to find an optimal solution to a problem with a set of constraints. Bio-Inspired Algorithms (BIAs) are based on biological behavior to solve a real world problem. BIA with optimization technique is to improve the overall performance of BIA. The aim of this paper is to introduce a novel optimization algorithm which is inspired by natural stinging behavior of honey bee to find the optimal solution. This algorithm performs both monitor and sting if any occurrence of predators. By applying a novel optimization algorithm based on stinging behavior of bee, used to solve the intrusion detection problems. In this paper, a new host intrusion detection system based on novel optimization algorithm has been proposed and implemented. The performance of the proposed Anomaly-based Host Intrusion Detection System (A-HIDS) using a novel optimization algorithm based on stinging behavior of bee has been tested. In this paper, after an explanation of the natural stinging behavior of honey bee, a novel optimization algorithm and A-HIDS are described and implemented. The results show that the novel optimization algorithm offers some advantage according to the nature of the problem.

M-ITRS: Mathematical Model for Identification of Tandem Repeats in DNA Sequence

Kumar, Ajay, Garhwal, Sunita

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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Abstract

In DNA, tandem repeat consists of two or more contiguous copies of a pattern of nucleotides. Tandem repeats of the motif are useful in many applications like molecular biology (related to genetic information of inherited diseases), forensic medicines, DNA fingerprinting and molecular markers for cancer. Various researchers designed formal models and grammars to identify two contiguous copies of the pattern. Tree-adjoining grammar cannot be designed for k-copy language. There is a need to design a formal model which will work for more than two contiguous copies of the pattern. In this paper, we have designed deep pushdown automata for k-continuous copies of the pattern for . The proposed formal model will also identify the tandem repeats without specifying the pattern and its size.

Overview of Model Free Adaptive (MFA) Control Technology

Takialddin, Al Smadi, Al-Agha, Osman Ibrahim, Alsmadi, Khalid Adnan

IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Original Source | Check in Google Scholar | Full PDF (344.542 KB)

Abstract

Model-Free Adaptive (MFA) control is a technology that has made a major impact on the automatic control industry. MFA control users have successfully solved many industry-wide control problems in various applications and achieved significant economic benefits. Now, the challenge is extending the many advantages of MFA control technology to diverse and fragmented markets, which could benefit from its unique capabilities. Since single-loop MFA controllers can directly replace legacy PID controllers without the need for "system" redesign (plugand play), they are readily embeddable in various instruments, equipment, and smart control valves. This alleviates concerns relative to cost of change and also makes MFA an appealing tool for OEM applications on a large scale.