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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN : 20894872     EISSN : 22528938     DOI : -
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 293 Documents
Performance Analysis of Granular Computing Model on the Basis of S/W Engineering and Data Mining Sasamal, Rajashree; Shial, Rabindra Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 4: December 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (252.416 KB)

Abstract

Granular Computing is not only a computing model for computer centered problem solving, but also a thinking model for human centered problem solving.Some authors have presented the structure of such kind models and investigated various perspectives of granular computing from different application  point of views.  In this paper we discuss the archeitectue of Granular computing  models, strategies, and applications. Especially, the perspectives of granular computing in various aspects as data mining and  phases of software engineering are presented, including recquirement specification system analysis and design, algorithm design,structured programming,software tesing.AI is used for measuring the three perspective  of Granular Computing model. Here we have discovered the patterns in sequence of events has been an area  of active research in AI. However, the focus in this body of work is on discovering the rule underlying the generation of a given sequence in order to be able to predict a plausible sequence continuation ( the rule to predict what number will come next, given a sequence of numbers).DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.1181
Artificial Intelligence: Way Forward for India Srivastava, Sunil Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424.533 KB) | DOI: 10.11591/ijai.v7.i1.pp19-32

Abstract

Artificial Intelligence (AI) is likely to transform the way we live and work. Due to its high potential, its adoption is being treated as the fourth industrial revolution. As with any major advancement in technology, it brings with it a spectrum of opportunities as well as challenges. On one hand, several applications have been developed or under development with potential to improve the quality of life significantly. As per a study, it is expected to double the annual economic growth rate of 12 developed countries by 2035. On the other hand, there is a possibility of loss of jobs. As per the available reports, the loss of jobs during the next 10-20 years is estimated to be 47% in the US, 35% in the UK, 49% in Japan, 40% in Australia, and 54% in the EU. In the era of globalization, no country can isolate itself from the impact of the advances in technology. However, the benefits can be maximized and losses can be minimized by putting necessary infrastructure and policy in place. Though several countries have decided their strategy for AI, India has not yet formulated its strategy. The report reviews the international as well as national scenario and suggests way forward for India. 
Prediction of Future Stock Close Price using Proposed Hybrid ANN Model of Functional Link Fuzzy Logic Neural Model Kumar. J, Kumaran; A, Kailas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.671 KB)

Abstract

In this paper, the prediction of future stock close price of SENSEX & NSE stock exchange is found using the proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model. The historic raw data’s of SENSEX & NSE stock exchange has been pre-processed to the range of (0 to 1). After pre-processing the inputs and forwarded to functional expansion function to perform neural operation. The activation function of neuron has fuzzy sets in order to show the future close price range of SENSEX & NSE stock exchange. The model is trained with the pre-processed historic data’s of stock exchange and the prediction rate (Performance & Error rate) of the Proposed Hybrid ANN model of Functional Link Fuzzy Logic Neural Model is calculated at the testing phase using the performance metrics (MAPE & RMSE).DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.362
Neural Network Controller for Power Electronics Circuits Rathi, K.J.; Ali, M. S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (525.235 KB) | DOI: 10.11591/ijai.v6.i2.pp49-55

Abstract

Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics. This paper explores the perspective of neural network applications in the intelligent control for power electronics circuits. The Neural Network Controller (NNC) is designed to track the output voltage and to improve the performance of power electronics circuits. The controller is designed and simulated using MATLAB-SIMULINK
An SVD based Real Coded Genetic Algorithm for Graph Clustering Roy, Parthajit; Mandal, Jyotsna Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 2: June 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (698.175 KB) | DOI: 10.11591/ijai.v5.i2.pp64-71

Abstract

This paper proposes a novel graph clustering model based on genetic algorithm using a random point bipartite graph. The model uses random points distributed uniformly in the data space and the measurement of distance from these points to the test points have been considered as proximity. Random points and test points create an adjacency matrix. To create a similarity matrix, correlation coefficients are computed from the given bipartite graph. The eigenvectors of the singular value decomposition of the weighted similarity matrix are considered and the same are passed to an elitist GA model for identifying the cluster centers. The model has been tasted with the standard datasets and the performance has been compared with existing standard algorithms.
Multi-agent System for Documents Retrieval and Evaluation Using Fuzzy Inference Systems Ivanova, Galina; Andreev, Ark; Shouman, Marwa A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (675.168 KB) | DOI: 10.11591/ijai.v5.i4.pp158-164

Abstract

Recently the World Wide Web are packed with huge quantities of information. From this view the user finds it difficult to get the relevant informations due to the increased of their quantities. This paper uses multi-agent system uses intelligent agent in order to retrieval documents from the World Wide Web. The user by this system can easily get the relevant documents which to need them.Multi-agent System is combined with fuzzy inference system for ranking documents. The documents ranking score by cosine similarity using fuzzy inference system development and implemented much simpler than the traditional method which require mathematical equations.
Classifying the EEG Signal through Stimulus of Motor Movement Using New Type of Wavelet Yulianto, Endro; Susanto, Adhi; Sri Widodo, Thomas; Wibowo, Samekto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 3: September 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (125.142 KB)

Abstract

Brain Computer Interface (BCI) refers to a system designed to translate the brain signal in controlling a computer application.  The most widely used brain signal is electroencephalograph (EEG) for using the non-invasive method, and having a quite good resolution and relatively affordable equipments. This research purposively is to obtain the characteristics of EEG signals using the motor movement of “turn right” and “turn left” that is by moving the simulation of steering wheel. The characteristic of signal obtained is subsequently used as a reference to create a new type of wavelet for classification. The signal processing, including a 4 – 20 Hz bandpass filter, signal segmentation in 1 to 2 seconds after stimuli and signal correlation,  is used to obtain the characteristic of EEG signal; namely Event–Related Synchronization /Desynchronization (ERS/ERD). The result of test data classification to two new types of wavelet shows that each volunteer has a higher correlation value towards the new type of wavelet that has been designed with various wavelet scales for each individuals.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.843
Estimation of water quality index using artificial intelligence approaches and multi-linear regression Gaya, Muhammad Sani; Abba, Sani Isah; Abdu, Aliyu Muhammad; Tukur, Abubakar Ibrahim; Saleh, Mubarak Auwal; Esmaili, Parvaneh; Wahab, Norhaliza Abdul
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.66 KB) | DOI: 10.11591/ijai.v9.i1.pp126-134

Abstract

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.
Fuzzy Logic Controller for Cascaded H-Bridge Multilevel Inverter Sivakumar, N.; Sumathi, A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.167 KB) | DOI: 10.11591/ijai.v4.i3.pp105-112

Abstract

This paper proposes fuzzy logic controller based seven-level hybrid inverter for photovoltaic systems with sinusoidal pulse width-modulation (SPWM) techniques. Multi-Level Inverter technology have been developed in the area of high-power medium-voltage energy scheme, because of their advantages such as devices of high dv/dt rating, higher switching frequency, unlimited power processing, shape of output waveform and desired level of output voltage, current and frequency adjustment.This topology can be used there by enabling the scheme to reduce the Total Harmonic Distortion (THD) for high voltage applications. The Maximum Power Point Tracking algorithm is also used for extracting maximum power from the PV array connected to each DC link voltage level. The Maximum Power Point Tracking algorithm is solved by Perturb and Observer method.It has high performance with low Total Harmonic Distortion and reduced by this control strategy. The proposed system has verified and THD is obtained by using MATLAB/simulink.The result is compared with the hardware prototype working model.
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System Tawafan, Adnan; Sulaiman, Marizan Bin; Ibrahim, Zulkifilie Bin
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)

Abstract

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

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