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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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Performance comparison of various probability gate assisted binary lightning search algorithm Md Mainul Islam; Hussain Shareef; Mahmood Nagrial; Jamal Rizk; Ali Hellany; Saiful Nizam Khalid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Recently, many new nature-inspired optimization algorithms have been introduced to further enhance the computational intelligence optimization algorithms. Among them, lightning search algorithm (LSA) is a recent heuristic optimization method for resolving continuous problems. It mimics the natural phenomenon of lightning to find out the global optimal solution around the search space. In this paper, a suitable technique to formulate binary version of lightning search algorithm (BLSA) is presented. Three common probability transfer functions, namely, logistic sigmoid, tangent hyperbolic sigmoid and quantum bit rotating gate are investigated to be utilized in the original LSA. The performances of three transfer functions based BLSA is evaluated using various standard functions with different features and the results are compared with other four famous heuristic optimization techniques. The comparative study clearly reveals that tangent hyperbolic transfer function is the most suitable function that can be utilized in the binary version of LSA.

Preventing Online Social Deception using Deception Matrix Alka Alka; Harjot Kaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

The organization collaboration is very important for the success of the organization. The persons who enter into the organization will interact with the other members of the organization. There exist leaders of the community who will be responsible for the management of the communication among the persons within the organization. Sometimes the information presented by the new person joining the community is not correct. That information will cause the deception over the network. In the purposed paper deception within the social media is going to be analyzed. Deception will cause legion of problems and sometimes death of the person who is deceived. The proposed paper suggests the mechanism for tackling such deceptions.

Large-scale image-to-video face retrieval with convolutional neural network features Imane Hachchane; Abdelmajid Badri; Aïcha Sahel; Yassine Ruichek
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Convolutional neural network features are becoming the norm in instance retrieval. This work investigates the relevance of using an of the shelf object detection network, like Faster R-CNN, as a feature extractor for an image-to-video face retrieval pipeline instead of using hand-crafted features. We use the objects proposals learned by a Region Proposal Network (RPN) and their associated representations taken from a CNN for the filtering and the re-ranking steps. Moreover, we study the relevance of features from a finetuned network. In addition to that we explore the use of face detection, fisher vector and bag of visual words with those CNN features. We also test the impact of different similarity metrics. The results obtained are very promising.

A Fuzzy Logic Based DSTATCOM for Diesel Generation System for Load Compensation JACOB PRABHAKAR BUSI; SRINIVASARAO YELAVARTHI
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 1: March 2015
Publisher : Institute of Advanced Engineering and Science

This paper proposes the concept of distributed static compensator for compensation of harmonics, unbalances and reactive powers. The main aim of this diesel electrical generator is to generate electrical power and transfer to the distribution point. The main problems occurred in this distribution systems are voltage distributions, interruptions and variations in distribution system also called as power quality problems. The FACTS controllers are classified into different types based on improvement of power quality. These facts devices are classified based on their construction and connection to the line i.e. called as series and shunt converters. This paper also concentrate on the concept of fuzzy logic controller for getting better performance as compared with the previous conventional controllers. Basically, the fuzzy controller has the advantage of low steady state error and also it reduces the These experimental diagrams are verified in Matlab/Simulink and the results are verified for both PI and Fuzzy controllers.

ANN based method for improving gold price forecasting accuracy through modified gradient descent methods Shilpa Verma; G. T. Thampi; Madhuri Rao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Forecast of prices of financial assets including gold is of considerable importance for planning the economy. For centuries, people have been holding gold for many important reasons such as smoothening inflation fluctuations, protection from an economic crisis, sound investment etc.. Forecasting of gold prices is therefore an ever important exercise undertaken both by individuals and groups. Various local, global, political, psychological and economic factors make such a forecast a complex problem. Data analysts have been increasingly applying Artificial Intelligence (AI) techniques to make such forecasts. In the present work an inter comparison of gold price forecasting in Indian market is first done by employing a few classical Artificial Neural Network (ANN) techniques, namely Gradient Descent Method (GDM), Resilient Backpropagation method (RP), Scaled Conjugate Gradient method (SCG), Levenberg-Marquardt method (LM), Bayesian Regularization method (BR), One Step Secant method (OSS) and BFGS Quasi Newton method (BFG). Improvement in forecasting accuracy is achieved by proposing and developing a few modified GDM algorithms that incorporate different optimization functions by replacing the standard quadratic error function of classical GDM. Various optimization functions investigated in the present work are Mean median error function (MMD), Cauchy error function (CCY), Minkowski error function (MKW), Log cosh error function (LCH) and Negative logarithmic likelihood function (NLG). Modified algorithms incorporating these optimization functions are referred to here by GDM_MMD, GDM_CCY, GDM_KWK, GDM_LCH and GDM_NLG respectively. Gold price forecasting is then done by employing these algorithms and the results are analysed. The results of our study suggest that the forecasting efficiency improves considerably on applying the modified methods proposed by us.

Type-2 Fuzzy Logic Control of a Doubly-Fed Induction Machine (DFIM) Loukal Keltoum; Benalia Leila
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 4: December 2015
Publisher : Institute of Advanced Engineering and Science

The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases have established their robust and that their performance is less sensitive to parameter variations over conventional controllers. In this paper, Interval Type-2 Fuzzy Logic Controller (IT2FLC) method is proposed for controlling the speed with a direct stator flux orientation control of doubly-fed induction motor (DFIM), we made a comparison between the Type-1 Fuzzy Logic Control (T1FLC) and IT2FLC of the DFIM, first a modeling of DFIM is expressed in a (d-q) synchronous rotating frame. After the development and the synthesis of a stabilizing control laws design based on IT2FLC. We use this last approach to the control of the DFIM under different operating conditions such as load torque and in the presence of parameter variation. The obtained simulation results show the feasibility and the effectiveness of the suggested method.

Genetic Algorithm for Grammar Induction and Rules Verification through a PDA Simulator Hari Mohan Pandey
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

The focus of this paper is towards developing a grammatical inference system uses a genetic algorithm (GA), has a powerful global exploration capability that can exploit the optimum offspring. The implemented system runs in two phases: first, generation of grammar rules and verification and then applies the GA’s operation to optimize the rules. A pushdown automata simulator has been developed, which parse the training data over the grammar’s rules. An inverted mutation with random mask and then ‘XOR’ operator has been applied introduces diversity in the population, helps the GA not to get trapped at local optimum. Taguchi method has been incorporated to tune the parameters makes the proposed approach more robust, statistically sound and quickly convergent. The performance of the proposed system has been compared with: classical GA, random offspring GA and crowding algorithms. Overall, a grammatical inference system has been developed that employs a PDA simulator for verification.

Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System Adnan Tawafan; Marizan Bin Sulaiman; Zulkifilie Bin Ibrahim
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 (782.725 KB)

High impedance fault (HIF) is abnormal event on electric power distribution feeder which does not draw enough fault current to be detected by conventional protective devices. The algorithm for HIF detection based on the amplitude ratio of second and odd harmonics to fundamental is presented. This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. It is integrating the learning capabilities of neural network to the fuzzy logic system robustness in the sense that fuzzy logic concepts are embedded in the network structure. It also provides a natural framework for combining both numerical information in the form of input/output pairs and linguistic information in the form of IF–THEN rules in a uniform fashion. Fast Fourier Transformation (FFT) is used to extract the features of the fault signal and other power system events. The effect of capacitor banks switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. The results show that the proposed algorithm can distinguish successfully HIFs from other events in distribution power systemDOI: http://dx.doi.org/10.11591/ij-ai.v1i2.425

Improvement of Power Quality Using Fuzzy Controlled D-STATCOM in Distribution System B. Santhosh Kumar; K.B. Madhu Sahu; K.B. Saikiran; CH. Krishna Rao
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

This paper investigates the problems associated with distribution system in terms of delivery of clean power and their solutions. Power quality has become a major issue in the present power system network. The network has mostly inductive nature. This draws more reactive power. This causes harmonics and voltage unbalance problems. So maintain the proper operation of interconnected power system, we are using one of the facts devices such as fuzzy controlled D-statcom. It provides suitable compensation and there by maintain proper power factor and also reduces harmonic contents. The simulation is taken out by MATLAB/SIMULINK and the result shows the effectiveness of GA (Genetic algorithm) simulation. Optimized Fuzzy controlled D-STATCOM for improvement of power quality.

Indexing Of Three Dimensions Objects Using GIST, Zernike & PCA Descriptors Driss Naji; Fakir Mohamed Fakir; O. Bencharef; B. Bouikhalene; A. Razouk
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 (316.153 KB)

In this paper, we present a new approach to object to recognition based on the combination of Zernike moments, descriptors Gist and PCA pair wise applied to color images. The recognition of objects are based on two approaches of classification the first use neural networks (NN) for learning stage and gratitude as well to the Support Vector Machines (SVM). The experimental results showed that the recognition by SVM is better than NN. We illustrate the proposed method on color images, including objects from the database COIL-100.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.825

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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
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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