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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 1,639 Documents

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A multiple mitosis genetic algorithm K. Kamil; K. H Chong; H. Hashim; S. A. Shaaya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum. This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.

Twitter Tweet Classifier Ashwin V
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

This paper addresses the task of building a classifier that would categorise tweets in Twitter. Microblogging nowadays has become a tool of communication for Internet users. They share opinion on different aspects of life. As the popularity of the microblogging sites increases the closer we get to the era of Information Explosion.Twitter is the second most used microblogging site which handles more than 500 million tweets tweeted everyday which translates to mind boggling 5,700 tweets per second. Despite the humongous usage of twitter there isn’t any specific classifier for these tweets that are tweeted on this site. This research attempts to segregate tweets and classify them to categories like Sports, News, Entertainment, Technology, Music, TV, Meme, etc. Naïve Bayes, a machine learning algorithm is used for building a classifier which classifies the tweets when trained with the twitter corpus. With this kind of classifier the user may simply skim the tweets without going through the tedious work of skimming the newsfeed.

Supervised evolutionary programming based technique for multi-DG installation in distribution system Muhammad Firdaus Shaari; Ismail Musirin; Muhamad Faliq Mohamad Nazer; Shahrizal Jelani; Farah Adilah Jamaludin; Mohd Helmi Mansor; A.V.Senthil Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Installing DG in network system, has supported the distribution system to provide the increasing number of consumer demand and load, in order to achieve that this paper presents an efficient and fast converging optimization technique based on a modification of traditional evolutionary programming method for obtain the finest optimal location and power loss in distribution systems. The proposed algorithm that is supervised evolutionary programming is implemented in MATLAB and apply on the 69-bus feeder system in order to minimize the system power loss and obtaining the best optimal location of the distributed generators.

Design and Development of an Agorithm for Prioritizing the Test Cases Using Neural Network as Classifier Amit Verma; simranjeet kaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 1: March 2015
Publisher : Institute of Advanced Engineering and Science

Test Case Prioritization (TCP) has gained wide spread acceptance as it often results in good quality software free from defects. Due to the increase in rate of faults in software traditional techniques for prioritization results in increased cost and time. Main challenge in TCP is difficulty in manually validate the priorities of different test cases due to large size of test suites and no more emphasis are made to make the TCP process automate. The objective of this paper is to detect the priorities of different test cases using an artificial neural network which helps to predict the correct priorities with the help of back propagation algorithm. In our proposed work one such method is implemented in which priorities are assigned to different test cases based on their frequency. After assigning the priorities ANN predicts whether correct priority is assigned to every test case or not otherwise it generates the interrupt when wrong priority is assigned. In order to classify the different priority test cases classifiers are used. Proposed algorithm is very effective as it reduces the complexity with robust efficiency and makes the process automated to prioritize the test cases.

Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm Nur Alisa Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction and communication skills. Diagnosis of ASD is one of the difficult problems facing researchers. This research work aimed to reveal the different pattern between autistic and normal children via electroencephalogram (EEG) by using the deep learning algorithm. The brain signal database used pattern recognition where the extracted features will undergo the multilayer perceptron network for the classification process. The promising method to perform the classification is through a deep learning algorithm, which is currently a well-known and superior method in the pattern recognition field. The performance measure for the classification would be the accuracy. The higher percentage means the more effectiveness for the ASD diagnosis. This can be seen as the ground work for applying a new algorithm for further development diagnosis of autism to see how the treatment is working as well in future.

Time-Based Raga Recommendation and Information Retrieval of Musical Patterns in Indian Classical Music Using Neural Networks Samarjit Roy; Sudipta Chakrabarty; Debashis De
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

In Indian Classical Music (ICM) perspective, Raga is formed from the different and correct combination of notes. If it is observed the history of Indian Classical Raga in ICM, the playing or serving each of the ragas has some unique sessions. The procedure is to suggest the classifications of playing a raga has been attempted to display by explaining unique musical features and pattern matching. This contribution has been represented how music structures can be advanced through a more conceptual demonstration and consent to unambiguously describe process of computational modeling of Musicology which signify the challenge on complete musical composition from the elementary vocal objects of ICM usage using Neural Networks. In Neural network the samples of various ragas have been taken as input and classify them according to the times of the performance. Over 90% accuracy level has achieved using entire Confusion Matrices and Error Histogram performance evaluation technique.

Firefly Algorithm Solution to Improving Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks Baghouri Mostafa; Chakkor Saad; Hajraoui Abderrahmane
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

The improvement of the lifetime of heterogeneous wireless sensor networks is a challenge for many researches. One of the most important protocols to achieve this goal is to divide the network into clusters that run by a single node called cluster head and the others have attached. However, all nodes must form the cluster including the nearest nodes to the base station which should be excluded from the clustering process. Furthermore these nodes consume more energy since each member node communicates directly with their cluster head and not with the base station. To eliminate these notes from cluster process, we need to formulate a new energy total of the network which depends on the number of these nodes. In this paper we propose a new technic to optimize this energy which basing on the firefly algorithm. The developed approach allows the boundary of the excluded nodes efficiently. Computer simulation in MATLAB proves the superiority of this method concerning the increase of the lifetime and the number of the received packet messages compared to the others protocols.

Solving University Scheduling Problem with a Memetic Algorithm Mortaza Abbaszadeh; Saeed Saeedvand; Hamid Asbagi Mayani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 2: June 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2713.506 KB)

Scheduling problem is one of the Non-deterministic Polynomial (NP) problems. This means that using a normal algorithm to solve NP problems is so time-consuming a process (it may take months or even years with available equipment), and thus such an algorithm is regarded as an impracticable way of dealing with NP problems. The method of Memetic Algorithm presented in this paper is different from other available algorithms. In this algorithm the problem of a university class Scheduling is solved through applying a new chromosome structure, modifying the normal genetic methods and adding a local search, which is claimed to considerably improve the solution. We included the teacher, class and course information with their maximal constraints in the proposed algorithm, and it produced an optimized scheduling table for a weekly program of the university after creating the initial population of chromosomes and running genetic operators. The results of the study show a high efficiency for the proposed algorithm compared with other algorithms considering maximum Constraints.DOI: http://dx.doi.org/10.11591/ij-ai.v1i2.512

Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data Manaswini Pradhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.

Enhancing Knowledge Hyper Surface Method for Casting Diagnosing Nazri Mohd Nawi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (832.727 KB)

The diagnosis of defective castings has always been a centre of attention in the manufacturing industry. This is mainly because the cause and effect relationship in a casting process is complex and non-linear. Furthermore, a large number of parameters are needed to be coordinated with each other in an optimal way to minimise the occurrence of defective castings. An intelligent diagnosis system is needed to diagnose effectively the causal representation and also justify its diagnosis. A previous method, known as the Knowledge Hyper-surface method which used Lagrange Interpolation polynomials has gained more popularity in learning cause and effect analysis in casting processes. The current method show that the belief value of the occurrence of cause with respect to the change in the belief value in the occurrence of effect can be modeled by linear, quadratic or cubic relationships and the method retained the advantages of neural networks and overcomes their limitations in learning the input-output mapping function in the presence of noisy, limited and sparse data. However, the methodology was unable to model exponential increase/decrease in belief values in cause and effect relationships. This paper proposed an enhancement to the current Knowledge Hyper-surface method by introducing midpoints in the existing shape formulation which further constrains the shape of the Knowledge hyper-surfaces to model an exponential rise in belief values but without exposing the dataset to the limitations of ‘over fitting’. The ability of the proposed method to capture the exponential change in the belief variation of the cause when the belief in the effect is at its minimum is compared to the current method on real casting data.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.584

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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

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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

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