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International Journal of Artificial Intelligence Research
Published by STMIK Dharma Wacana
ISSN : -     EISSN : 25797298     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 2, No 2 (2018): December 2018" : 5 Documents clear
Bio-inspired Expert System based on Genetic Algorithm for Printer Identification in Forensic Science Darwish, Saad Mohamed; ELgohary, Hany M
International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (777.591 KB) | DOI: 10.29099/ijair.v2i2.67

Abstract

Printer identification models are provided for the goal of distinguishing the printer that produced a suspicious imprinted document. Source identification of a published document can easily be a significant procedure intended for the forensic science. The arising problem is that the extraction of many features of the printed document for printer identification sometimes increases time and reduces the classification accuracy since a lot of the document features may come to be repetitive and non-beneficial. Distinct combinatorial collection of features will need to be acquired in order to preserve the most effective fusion to accomplish the maximum accuracy. This paper presents an intelligent machine learning algorithm for printer identification that adopts both of texture features formulated from gray level co-occurrence matrix of the printed letter ''WOO'' and genetic heuristic search to select the optimal reduced feature set. This integration aims to achieve high classification accuracy based on small group of discriminative features. For classification, the system utilizes k-nearest neighbors (KNN) to recognize the source model of the printer for its simplicity. Experimental results validate that the suggested system has high taxonomy accuracy and requires less computation time.
Distance and Fuzzy Classifiers Alliance: The Solution to Off-line Arabic Signature Verification System for Forensic Science Darwish, Saad Mohamed; Noori, Zainab H
International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1258.98 KB) | DOI: 10.29099/ijair.v2i2.66

Abstract

Signature of a person is one of the most popular and legally accepted behavioral biometrics that provides secure means for verification and personal identification in many applications such as financial, commercial and legal transactions. The objective of the signature verification system is to classify between genuine and forgery that is often associated with intrapersonal and interpersonal variability. Unlike other languages, Arabic has unique features; it contains diacritics, ligatures, and overlapping.  Because of lacking any form of dynamic information during the Arabic signature writing process, it will be more difficult to obtain higher verification accuracy. This paper addresses the above difficulty by introducing a novel Off-Line Arabic signature verification algorithm. Different from state-of-the-art works that adopt one-level of verification or multiple classifiers based on statistical learning theory; this work employs two-level of fuzzy set related verification. The level one verification depends on finding the total difference between the features extracted from the test signature and the mean values of each corresponding features in the training signatures (owning the same signature). Whereas, the level two verification relies on the output of the fuzzy logic module depending on the membership functions that has been created from the signature features in the training dataset for a specific signer. It is concluded from the experimental results that the verification system performs well and has the ability to reduce both False Acceptance Rate (FAR) and False Rejection Rate (FRR).
Application Of Backpropagation Neural Networks In Predicting Rainfall Data In Ambon City Yopi Andry Lesnussa; C. G. Mustamu; F. Kondo Lembang; M. W. Talakua
International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.013 KB) | DOI: 10.29099/ijair.v2i2.59

Abstract

The Artificial Neural Networks is a process of information system on certain traits which as representatives of the human neural networks. The Artificial Neural Networks can be applied in every area of human life, one of them is environment especially about prediction of climate or weather. In this research, the artificial neural network is used to predict the rainfall with Backpropagation method and using MATLAB software. The other meteorology parameters used to predict the rainfall are air temperature, air velocity and air pressure. The result showed less accuracy level is 80% by using alpha 0,7, iteration number (epoch) 10000 and MSE value = 0,0218. Therefore, the result of rainfall prediction system is accurate.
A Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery Faiz, Alfian; Subiyanto, Subiyanto; Arief, Ulfah Mediaty
International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (756.717 KB) | DOI: 10.29099/ijair.v2i2.71

Abstract

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection  Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company.
Solution Search Simulation The Shortest Step On Chess Horse Using Breadth-First Search Algorithm Bastian, Ade; Nugraha, Rezha
International Journal of Artificial Intelligence Research Vol 2, No 2 (2018): December 2018
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.887 KB) | DOI: 10.29099/ijair.v2i2.58

Abstract

Horse seed in the chess board movement resembles the letter L. The chess pieces are one of a very hard-driven beans and seeds are often also the most dangerous if not carefully considered every movement. Simulation of this problem provides a chess board size n x n. Target (goal) of this problem is to move a horse beans of a certain position on a chess board position to the desired destination with the shortest movement simulates all possible solutions to get to the goal position. This problem is also one of the classic problems in artificial intelligence (AI). Settlement of this problem can use the help system and tree production tracking.Therefore, designed a simulation applications by utilizing several techniques of simulation programming and Breadth-First Search method. With this method, all nodes will be traced and the nodes at level n will be visited first before visiting the nodes at level n + 1. The purpose of this study is to design a software that is able to find all the solutions for the shortest movement toward the goal position by using the system of production and tracking tree.Results from this paper is that the software is able to find all solutions shortest movement a horse beans from the initial position to the goal position and displays the simulation of the movement of the horse in the chess board.

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