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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 1,722 Documents

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Development of path planning algorithm of centipede inspired wheeled robot in presence of static and moving obstacles using modified critical-snakebug algorithm Subir Kumar Das; Ajoy Kumar Dutta; Subir Kumar Debnath
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Path planning for a movable robot in real life situation has been widely cultivated and become research interest for last few decades. Biomimetic robots have increased attraction for their capability to develop various kind of walking in order to navigate in different environment. To meet this requirement of natural insect locomotion has enabled the development of composite tiny robots. Almost all insect-scale legged robots take motivation from stiff-body hexapods; though, a different distinctive organism we find in nature is centipede, distinguished by its numerous legs and pliable body. This uniqueness is anticipated to present performance benefits to build robot of the said type in terms of swiftness, steadiness, toughness, and adaptation ability. This paper proposes a local path planning algorithm of multiple rake centipede inspired robot namely ModifiedCritical-SnakeBug (MCSB) algorithm. Algorithm tries to avoid static and dynamic obstacle both. The results demonstrate the capability of the algorithm.

Visual Surveillance for Hajj and Umrah: A Review Yasir Salih Ali; Mohammed Talal Simsim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 2: June 2014
Publisher : Institute of Advanced Engineering and Science

This paper presents advances on crowd management research with specific interest on high density crowds such as Hajj and Umrah crowds. In the past few years, there has been increasing interest in pursuing video analytics and visual surveillance to improve the security and safety of pilgrimages during their stay in Mecca. Despite the fact that visual surveillance research has matured significantly in the rest of the world and had been implemented in many scenarios, research on visual surveillance for Hajj and Umrah application still remains at its early stages and there are many issues that need to be addressed in future research. This is mainly because Hajj is a very unique event that shows the clustering of millions of people in small area where most advanced image processing and computer vision algorithms fail to generate accurate analysis of the image content. There is a strong need to develop new algorithms specifically tailored for Hajj and Umrah applications. This review aims to give attentions to these interesting future research areas based on analysis of current visual surveillance research. The review also pinpoint to pioneer techniques on visual surveillance in general that can be customized to Hajj and Umrah applications.

An efficient method to improve the clustering performance using hybrid robust principal component analysis-spectral biclustering in rainfall patterns identification Shazlyn Milleana Shaharudin; Shuhaida Ismail; Siti Mariana Che Mat Nor; Norhaiza Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and biclustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65%-70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCAkMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result.

Automatic Exudates Detection in Diabetic Retinopathy Images H. Faouzi; Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 2: June 2016
Publisher : Institute of Advanced Engineering and Science

Diabetic Retinopathy (DR) refers to the presence of typical retinal micro vascular lesions in persons with diabetics. When the disease is at the early state, a prompt diagnosis may help in preventing irreversible damages to the diabetic eye. If the exudates are closer to macula, then the situation is critical. Early detection can potentially reduce the risk of blind. This paper proposes tool for the early detection of Diabetic Retinopathy using edge detection, algorithm kmeans in segmentation phase, invariant moments (Hu and Affine) and descriptor GIST in extraction phase. In the recognition phase, neural network is adopted. All tests are applied on database DIARETDB1.

Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving Wan Nazirah Wan Md Adnan; Nofri Yenita Dahlan; Ismail Musirin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving.

A Novel Neuro-Fuzzy Controller for Multilevel Renewable Energy System T PAVAN KUMAR; B N KARTHEEK
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 1: March 2015
Publisher : Institute of Advanced Engineering and Science

Recently, Development and the utilization of single phase based multilevel inverters has been increased. This paper proposes concept based new topology based seven level inverter with less number of power electronics switches with utility grid connection. This proposed multilevel inverter operates with only eight power electronics switches at their fundamental frequency. This inverter produces seven level output from the input here we considered as a photovoltaic system. The cost, complexity, switching losses are small due to because of usage of less number of switches. The DC/DC converter receives input from which the three positive output voltages are generated and the multilevel inverter performs as a polarity reversal that provides both the positive and negative cycle output. For further enhancement in the output waveform, the filter circuit can be integrated in the output terminal of the multilevel inverter. This paper also proposed a concept of a neuro-fuzzy controller for controlling the seven level inverter. The simulation results are observed by means of MATLAB simulink toolbox.

Estimation of water quality index using artificial intelligence approaches and multi-linear regression Muhammad Sani Gaya; Sani Isah Abba; Aliyu Muhammad Abdu; Abubakar Ibrahim Tukur; Mubarak Auwal Saleh; Parvaneh Esmaili; Norhaliza Abdul Wahab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Water quality index is a measure of water quality at a certain location and over a period of time. High value indicates that the water is unsafe for drinking and inadequate in quality to meet the designated uses. Most of the classical models are unreliable producing unpromising forecasting results. This study presents Artificial Intelligence (AI) techniques and a Multi Linear Regression (MLR) as the classical linear model for estimating the Water Quality Index (WQI) of Palla station of Yamuna river, India. Full-scale data of the river were used in validating the models. Performance measures such as Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Determination Coefficient (DC) were utilized in evaluating the accuracy and performance of the models. The obtained result depicted the superiority of AI models over the MLR model. The results also indicated that, the best model of both ANN and ANFIS proved high improvement in performance accuracy over MLR up to 10% in the verification phase. The difference between ANN and ANFIS accuracy is negligible due to a slight increment in performance accuracy indicating that both ANN and ANFIS could serve as reliable models for the estimation of WQI.

A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes D. Geraldine Bessie Amali; Dinakaran M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 1: March 2017
Publisher : Institute of Advanced Engineering and Science

Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization.

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

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.

A Projection Algorithm to Detect Cancer Using Microarray Nazario D. Ramirez-Beltran; Joan Manuel Castro; Harry Rodriguez
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 (373.276 KB)

The projection algorithm to classify tissues with a large number of genes and a small number of microarrays is proposed.The algorithm is based on the angle formed by two vectors in the n-dimensional space, and takes advantages of the geometrical projection principle.The properties of known tissues can be used to train the algorithm and distinguish between the cancer and normal gene expressions.The gene’s percentiles from an independent data set can be used to create a third vector, which is projected into the previously trained vectors to classify the third vector in one of the two populations, cancer or normal population.The proposed algorithm was implemented to detect cervical cancer in a microarray data set, which contains 8 normal and 25 cancerous tissues, which were randomly selected one thousand of times using a combinatory strategy.The algorithm was compared with three existing algorithms that have been used to solve the microarray classification problem: Fisher discriminate function, logistic regression, and artificial neural networks.Results show that the proposed algorithm outperformed the selected algorithmsDOI: http://dx.doi.org/10.11591/ij-ai.v1i2.469

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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

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Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

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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