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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 10 Documents

Search results for , issue "Vol 8, No 2: June 2019" : 10 Documents clear

Artificial bee colony algorithm used for load balancing in cloud computing: review Arif Ullah; Nazri Mohd Nawi; Jamal Uddin; Samad Baseer; Ansam Hadi Rashed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Cloud computing is emerging technology in IT land. But it still faces challenges like load balancing. It is a technique which dynamic distributed work load among various nodes equally in a situation where some nodes are under load and some are overload. Main achievements of load balancing are resource consumption and reduce energy. Swarm intelligence provides an important role in the field of those problems which cannot easily solve and they need classical and mathematical technique. An artificial bee colony is a foraging behavior inspires algorithm it established by karaboga in 2005. It has fast convergence, strong, robustness, and high flexibility. The different researcher used ABC algorithm for improvement in load balancing. This review paper is a comprehensive study about load balancing in cloud computing using ABC algorithm. It also defines some basic concept about swarm intelligent and its property.

Adaptive real time traffic prediction using deep neural networks Parinith R Iyer; Shrutheesh Raman Iyer; Raghavendran Ramesh; Anala M R; K.N. Subramanya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting adaptive traffic light systems, instead of fixedtime traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. The count of each class (2-wheelers, cars, trucks, buses etc.) is used to predict the signal green-time for the next cycle. The model selfadjusts every cycle by utilizing weighted moving averages. This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.

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.

Hybrid imperialistic competitive algorithm incorporated with hopfield neural network for robust 3 satisfiability logic programming Vigneshwer Kathirvel; Mohd. Asyraf Mansor; Mohd Shareduwan Mohd Kasihmuddin; Saratha Sathasivam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Imperialist Competitive algorithm (ICA) is a robust training algorithm inspired by the socio-politically motivated strategy. This paper focuses on utilizing a hybridized ICA with Hopfield Neural Network on a 3- Satisfiability (3-SAT) logic programming. Eventually the performance of the proposed algorithm will be compared to other 2 algorithms, which are HNN3SATES (ES) and HNN-3SATGA (GA). The performance shall be evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squares Error (SSE), Schwarz Bayesian Criterion (SBC), Global Minima Ratio and Computation Time (CPU time). The expected outcome will portray that the IC algorithm will outperform the other two algorithms in doing 3-SAT logic programming.

Privileged authenticity in reconstruction of digital encrypted shares Joydeep Dey; Anirban Bhowmik; Arindam Sarkar; Sunil Karforma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Efficient message reconstruction mechanism depends on the entire partial shares received in random manner. This paper proposed a technique to ensure the authenticated accumulation of shares based on the privileged share. Threshold number of received shares inclusive of the privileged share, were being accumulated together to validate the original message. Although attaining threshold number of shares or more excluding the privileged share, it would not be possible to reconstruct the original message. Encryptional procedure has been put into the desired partial shares to confuse the evaesdroppers. Decisive parameter termed as hash tag has been extracted from the cumulative shares and bitwise checking procedure has been carried out. In appearance of first mismatch, rests of the checking bits were ignored, as test case put under failure transaction. Different statistical tests namely floating frequency, entropy value have proved the robustness of the proposed technique. Thus, extensive experiments were conducted to evaluate the security and efficiency with better productivity.

An improved radial basis function networks based on quantum evolutionary algorithm for training nonlinear datasets Lim Eng Aik; Tan Wei Hong; Ahmad Kadri Junoh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms.This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.

Overlapped music segmentation using a new effective feature and random forests Duraid Y. Mohammed; Khamis A. Al-Karawi; Philip Duncan; Francis F. Li
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v8.i2.pp181-189

In the field of audio classification, audio signals may be broadly divided into three classes: speech, music and events. Most studies, however, neglect that real audio soundtracks can have any combination of these classes simultaneously. This can result in information loss, thus compromising the knowledge discovery. In this study, a novel feature, “Entrocy”, is proposed for the detection of music in both pure form and overlapping with the other audio classes. Entrocy is defined as the variation of the information (or entropy) in an audio segment over time. Segments, which contain music, were found to have lower Entrocy since there are fewer abrupt changes over time. We have also compared Entrocy with existing music detection features and the entrocy showing a good performance.

Sensitivity analysis of a species conserving genetic algorithm's parameters for addressing the niche radius problem Michael Scott Brown
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Niche Genetic Algorithms (NGA) are a special category of Genetic Algorithms (GA) that solve problems with multiple optima. These algorithms preserve genetic diversity and prevent the GA from converging on a single optima. Many NGAs suffer from the Niche Radius Problem (NRP), which is the problem of correctly setting a radius parameter for optimal results. While the selection of the radius value has been widely researched, the effects of other GA parameters on genetic diversity is not well known. This research is a parameter sensitivity analysis on the other parameters in a GA, namely mutation rate, number of individuals and number of generations.

Prediction of bankruptcy using big data analytic based on fuzzy c-means algorithm Arup Guha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

This paper has suggested an optimization approach of the cluster-based sampling using Fuzzy c means algorithm to the classifier in order to select the most appropriate instances of bankruptcy. This method was examined with the help of a clustering method and GA based artificial neural network in order to solve the existing data imbalance issue. The objective of this paper is to optimize the selected design model of GA-ANN by using Fuzzy C means algorithm to predict corporate bankruptcies by considering different financial ratios of companies across several industries within the period from 1994 to 2014. Effectiveness of this method was proved by comparing its accuracy rate with the results of existing method. From the performance result the accuracy rate of this method was found to be 78.2% and misclassification rate to be 0.2178.

Suggestive GAN for supporting Dysgraphic drawing skills Smita Pallavi; Akash Kumar; Abhinav Ankur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

The squat competence of dysgraphia affected students in drawing graphics on paper may deter the normal pace of learning skills of children. Convolutional neural network may tend to extract and stabilize the actionmotion disorder by reconstructing features and inferences on natural drawings. The work in this context is to devise a scalable Generative Adversarial Network system that allows training and compilation of image generation using real time generated images and Google QuickDraw dataset to use quick and accurate modalities to provide feedback to empower the guiding software as an apt substitute for human tutor. The training loss accuracy of both discriminator and generator networks is also compared for the SGAN optimizer.

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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

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