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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 6 Documents

Search results for , issue "Vol 3, No 1: March 2014" : 6 Documents clear

Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine Farzin Piltan; Mansour Bazregar; Marzieh Kamgari; Mojdeh Piran; Mehdi Akbari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

In this research, manage the Internal Combustion (IC) engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.

Fuzzy Black Holes Algorithm Mostafa Nemati; Reza Salimi; Behdad Moshref
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

In this paper fuzzy version for black holes algorithm is proposed. The main idea of this article is based upon this principle that we should consider the distance between two black holes for calculating gravitational force (global search) and electrical force (local search). For this purpose, we have suggested Fuzzy distance notion. In this proposed idea, for calculating two forces, FQ and FG, considering the distance between black holes, we have defined a Fuzzy function, which receives distance value and depending on this value being low or high, produces a membership degree for gravitational and electrical constants to be used in the formulas related to the calculation of FG and FQ. The proposed method is verified using several benchmark problems used in the area of optimization. The experimental results on different benchmarks show that the performance of the proposed algorithm is better than basic BLA (Black holes Algorithm) and FPSO (fuzzy Particle Swarms Optimization).

Utilizing CommonKADS as Problem-Solving and Decision-Making for Supporting Dynamic Virtual Organization Creation Morcous M. Yassa; Hesham A. Hassan; Fatma A. Omara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

Business Opportunity (BO) needs business collaboration and rapid distributed solution. Legacy systems are not enough to cope with it and there is a need to create Dynamic Virtual Organizations (DVO). While ecosystems have no agree in this area of business markets, some earlier DVO work used ecosystems to handle BO. The main objective of this paper is to show how CommonKADS knowledge engineering methodology is used to model DVO; life cycle, identification, and formation. Towards this objective, different perspectives used to analyze Collaboration Network Organization (CNO) have been discussed. Also, four more perspectives (CNO boundary fixing, organizational behavior, CNO federation modeling, and external environments) have been suggested to obtain what we called a Federated CNO Model (FCNOM). We believe that according to the work in this paper, the negotiations within CNO components during its life cycle will be minimized, the DVO configuration automation will be support, and more harmonization between CNO partners will be accomplished.

A Fast Genetic Algorithm for Solving University Scheduling Problem Mortaza Abbaszadeh; Saeed Saeedvand
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

University course timetabling is a NP-hard problem which is very difficult to solve by conventional methods, we know scheduling problem is one of the Nondeterministic Polynomial (NP) problems. This means, solving NP problems through normal algorithm is a time-consuming process (it takes days or months with available equipment) which makes it impossible to be solved through a normal algorithm like this. In purposed algorithm the problem of university class scheduling is solved through a new chromosome structure and modifying the normal genetic methods which really improves the solution in this case. We include lecturer, class and course information in presented algorithm, with all their Constraints, and it creates optimized scheduling table for weekly program of university after creating primary population of chromosomes and running genetic operators. In the final part of this paper we conclude from the results of input data analysis that the results have high efficiency compared with other algorithms considering maximum Constraints.

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor Minakshi Sharma; Saourabh Mukherjee
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties. Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.

Prediction of cutting and feed forces for conventional milling process using adaptive neuro fuzzy inference system (ANFIS) Kanhu Charan Nayak; Rajesh Kumar Tripathy; Sudha Rani Panda; Shiba Narayan Sahoo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

Due to the extensive use of highly automated machine tools in the industry, the manufacturing requires reliable models for the prediction of output performance of machining processes. The prediction of cutting forces plays an important role in the manufacturing industry. The focus of this paper is to develop a reliable method to predict cutting forces (force in X-direction and force in Z-direction) for milling process during conventional machining of mild steel. This paper implements an adoptive Neuro-fuzzy interface system (ANFIS) to actualize an efficient model for prediction of cutting forces during conventional milling. A set of three input machining parameters like speed, feed and depth of cut, which has a major impact on the cutting forces was chosen as input to represent the machining condition. Our result confirms that ANFIS model with Gaussian member function is a better predictive tool for prediction of milling forces with minimum average test error.

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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

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

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