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IAES International Journal of Artificial Intelligence (IJ-AI)

IJAI
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Published by Institute of Advanced Engineering and Science

ISSN : 20894872 EISSN : 22528938 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Engineering

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.

Arjuna Subject : -

Articles 6 Documents

Search results for , issue "Vol 7, No 4: December 2018" : 6 Documents clear

Dwindling of Real Power Loss by Enriched Big Bang-Big Crunch Algorithm K. Lenin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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.

A Novel Optimization Algorithm Based on Stinging Behavior of Bee S. Jayalakshmi; R. Aswini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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.

Overview of Model Free Adaptive (MFA) Control Technology Al Smadi Takialddin; Osman Ibrahim Al-Agha; Khalid Adnan Alsmadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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.

Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor Mohammed Sarhan Al_Duais; Fatma Susilawati. Mohamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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.

M-ITRS: Mathematical Model for Identification of Tandem Repeats in DNA Sequence Ajay Kumar; Sunita Garhwal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

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.

Comparison of Neural Network Training Algorithms for Classification of Heart Diseases Hesam Karim; Sharareh R. Niakan; Reza Safdari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 4: December 2018
Publisher : Institute of Advanced Engineering and Science

Heart disease is the first cause of death in different countries. Artificial neural network (ANN) technique can be used to predict or classification patients getting a heart disease. There are different training algorithms for ANN. We compared eight neural network training algorithms for classification of heart disease data from UCI repository containing 303 samples. Performance measures of each algorithm containing the speed of training, the number of epochs, accuracy, and mean square error (MSE) were obtained and analyzed. Our results showed that training time for gradient descent algorithms was longer than other training algorithms (8-10 seconds). In contrast, Quasi-Newton algorithms were faster than others (<=0 second). MSE for all algorithms was between 0.117 and 0.228. While there was a significant association between training algorithms and training time (p<0.05), the number of neurons in hidden layer had not any significant effect on the MSE and/or accuracy of the models (p>0.05). Based on our findings, for development an ANN classification model for heart diseases, it is best to use Quasi-Newton training algorithms because of the best speed and accuracy.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
-

Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Imam Much Ibnu Subroto

Contact Email imam@unissula.ac.id

Phone -

Journal Mail Official ijai@iaesjournal.com

Editorial Address -

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
-

Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA