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IAES International Journal of Artificial Intelligence (IJ-AI)
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 5 Documents
 1 
Search results for , issue "Vol 6, No 4: December 2017" : 5 Documents clear
Identifying Risk Factors of Diabetes using Fuzzy Inference System Lazim Abdullah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (754.453 KB) | DOI: 10.11591/ijai.v6.i4.pp150-158

Abstract

Identification of the real risk factors of diabetes is still very much inconclusive. In this paper, fuzzy rules based system was devised to identify risk factors of diabetes. The system consists of five input variables: Body Mass Index, age, blood pressure, Creatinine, and serum cholesterol and one output variable: level of risk. Three Gaussian membership functions for linguistic terms are defined for each input variable. The level of risk is defined using three triangular membership functions to represent output variable. Based on the information from patients’ clinical audit reports, the system was used to classify the level of risk of fifty patients that currently undergoing regular diagnosis for diabetes treatment. The system successfully classified the risk into three levels of Low, Medium and High where three main contributing factors toward developing diabetes were identified. The three risk factors are age, blood pressure and serum cholesterol. The multi-input system that characterised by IF-THEN fuzzy rules provide easily interpretable result for identifying predictors of diabetes. Research to establish reproducibility and validity of the findings are left for future works.
Fuzzy-Set Based Privacy Preserving Access Control Techniques in Cloud (FB-PPAC) Sushmita Kumari; Sudha S; Brindha K
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.293 KB) | DOI: 10.11591/ijai.v6.i4.pp143-149

Abstract

The word “Cloud” refers to network or internet. It is present at.remote location. Cloud computing is a latest mechanism used now-a-days for accessing, manipulating and configuring applications online via internet. It allows users for online data storage, various applications and infrastructure. There are few downsides of cloud computing like in public cloud sharing of data, selected data shared with users of various level without confidentiality and privacy of data. Different methods were used to fix this problem like encryption of attribute; encryption of access control but they have their own problems related to big computation for accquiring access structure, invoking and behavior management. So for removing these weakness, the combination of fuzzy-set theory and RSA algorithm has been introduced. Fuzzy-set is used for clustering the data based on their points. Further for privacy, I have included RSA for encryption and decryption of data which is used to store in cloud database. The analysis of my experiment shows the system is efficient, flexible and provides confidentiality of the data.
Identification of Rare Genetic Disorder from Single Nucleotide Variants Using Supervised Learning Technique Sathyavikasini K; Vijaya M S
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (526.637 KB) | DOI: 10.11591/ijai.v6.i4.pp174-184

Abstract

Muscular dystrophy is a rare genetic disorder that affects the muscular system which deteriorates the skeletal muscles and hinders locomotion. In the finding of genetic disorders such as Muscular dystrophy, the disease is identified based on mutations in the gene sequence. A new model is proposed for classifying the disease accurately using gene sequences, mutated by adopting positional cloning on the reference cDNA sequence. The features of mutated gene sequences for missense, nonsense and silent mutations aims in distinguishing the type of disease and the classifiers are trained with commonly used supervised pattern learning techniques.10-fold cross validation results show that the decision tree algorithm was found to attain the best accuracy of 100%. In summary, this study provides an automatic model to classify the muscular dystrophy disease and shed a new light on predicting the genetic disorder from gene based features through pattern recognition model.
Neural KDE Based Behaviour Model for Detecting Intrusions in Network Environment V. Brindha Devi; K.L. Shunmuganathan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.178 KB) | DOI: 10.11591/ijai.v6.i4.pp166-173

Abstract

Network intrusion is one of the growing concern throughout the globe about the information stealing and data exfiltration. In recent years this was coupled with the data exfiltration and infiltration through the internal threats. Various security encounters have been taken in order to reduce the intrusion and to prevent intrusion, since the stats reveals that every 4 seconds, at least one intrusion is detected in the detection engines. An external software mechanism is required in order to detect the network intrusions. Based on the above stated problem, here we proposed a new hybrid behaviour model based on Neural KDE and correlation method to detect intrusions. The proposed work is splitted into two phases. Initial phase is setup with the Neural KDE as the learning phase and the basic network parameters are profiled for each hosts, here the neural KDE is generated based on the input and learned parameters of the network. Next phase is the detection phase, here the Neural KDE is computed for the identified parameters and the learned KDE feature value is correlated with the present KDE values and correlated values are calculated using cross correlation method. Experimental results show that the proposed model is robust in detecting the intrusions over the network.
Classification of Road Damage from Digital Image Using Backpropagation Neural Network Sutikno Sutikno; Helmie Arif Wibawa; Prima Yusuf Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (544.253 KB) | DOI: 10.11591/ijai.v6.i4.pp159-165

Abstract

One of the biggest causes of death in the world is a traffic accident. Road damage is one of the cause factors from the traffic accident. To reduce this problem is required an early detection against road damage. This paper describes how to classify road damage using image processing and backpropagation neural network. Image processing is used to obtain binary image consists of a normalization, grayscaling, edge detection and thresholding, while the backpropagation neural network algorithm is used for classifying. The conclusion of this test that the algorithm is able to provide the accuracy rate of 83%. The results of this research may contribute to the development of road damage detection system based on the digital image so that the traffic accidents caused by road damage can be reduced.

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