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IAES International Journal of Artificial Intelligence (IJ-AI)

IJAI
Website

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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Takagi-Sugeno Fuzzy Perpose as Speed Controller in Indirect Field Oriented Control of Induction Motor Drive Roslina Mat Ariff; Dirman Hanafi; Wahyu Mulyo Utomo; Nooradzianie Muhammad Zin; Sy Yi Sim; Azuwien Aida Bohari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

This paper deal with the problem in speed controller for Indirect Field Oriented Control of Induction Motor. The problem cause decrease performance of Induction Motor where it widely used in high-performance applications. In order decrease the fault of speed induction motor, Takagi-Sugeno type Fuzzy logic control is used as the speed controller. For this, a model of indirect field oriented control of induction motor is built and simulating using MATLAB simulink. Secondly, error of speed and derivative error as the input and change of torque command as the output for speed control is applied in simulation. Lastly, from the simulation result overshoot is zero persent, rise time is 0.4s and settling time is 0.4s. The important data is steady state error is 0.01 percent show that the speed can follow reference speed. From that simulation result illustrate the effectiveness of the proposed approach.

Neural KDE Based Behaviour Model for Detecting Intrusions in Network Environment V. Brindha Devi; K.L. Shunmuganathan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Network intrusion is one of the growing concern throughout the globe about the information stealing and data exfiltration. In recent years this was coupled with the data exfiltration and infiltration through the internal threats. Various security encounters have been taken in order to reduce the intrusion and to prevent intrusion, since the stats reveals that every 4 seconds, at least one intrusion is detected in the detection engines. An external software mechanism is required in order to detect the network intrusions. Based on the above stated problem, here we proposed a new hybrid behaviour model based on Neural KDE and correlation method to detect intrusions. The proposed work is splitted into two phases. Initial phase is setup with the Neural KDE as the learning phase and the basic network parameters are profiled for each hosts, here the neural KDE is generated based on the input and learned parameters of the network. Next phase is the detection phase, here the Neural KDE is computed for the identified parameters and the learned KDE feature value is correlated with the present KDE values and correlated values are calculated using cross correlation method. Experimental results show that the proposed model is robust in detecting the intrusions over the network.

Performance Analysis Of Clustering Protocol Using Fuzzy Logic For Wireless Sensor Network Vaibhav Godbole
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 (963.559 KB)

In order to gather information more efficiently, wireless sensor networks are partitioned into clusters. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop wireless sensor networks. In this paper, we analyze a fuzzy clustering algorithm which aims to prolong the lifetime of wireless sensor networks. This algorithm adjusts the cluster-head radius considering the residual energy and the distance to the base station parameters of the sensor nodes. This helps decreasing the intra-cluster work of the sensor nodes which are closer to the base station or have lower battery level. In this paper fuzzy logic is utilized for handling the uncertainties in cluster-head radius estimation. We compare this algorithm with LEACH according to first node dies, half of the nodes alive and energy-efficiency metrics. Our simulation results show that the fuzzy clustering approach performs better than LEACH. Therefore, the fuzzy clustering algorithm is a stable and energyefficient clustering algorithm.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.1259

Security Solutions Using Brain Signals Anupama. H.S; Anusha M; Aparna Joshi; Apoorva N; N.K. Cauvery; Lingaraju. G.M
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

A Brain Computer Interface is a direct neural interface or a brain–machine interface. It provides a communication path between human brain and the computer system. It aims to convey people's intentions to the outside world directly from their thoughts. This paper focuses on current model which uses brain signals for the authentication of users. The Electro- Encephalogram (EEG) signals are recorded from the neuroheadset when a user is shown a key image (signature image). These signals are further processed and are interpreted to obtain the thought pattern of the user to match them to the stored password in the system. Even if other person is presented with the same key image it fails to authenticate as the cortical folds of the brain are unique to each human being just like a fingerprint or DNA.

Plant Leaf Disease Image Retrieval Using Color Moments Jayamala Kumar Patil; Raj Kumar
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 (315.65 KB)

This paper presents a Content Based Image Retrieval (CBIR)method for plant leaf image retrieval, intended for identification of leaf diseases. We used color features to extract the contents of leaf images. The color features are extracted using first three color moments. For similarity measurement median value of moment feature vectors is used. The method is studied for three different color spaces i.e. RGB, HSV & YCbCr. The experimental result shows that HSV color space provides better results for plant leaf disease retrieval.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1319

Academic performance prediction algorithm based on fuzzy data mining Anil Kumar Tiwari; G. Ramakrishna; Lokesh Kumar Sharma; Sunil Kumar Kashyap
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents an algorithm for prediction of academic performance of students by fuzzy data mining. The fuzzy-trace concept applied to predict the academic performance of the students. An algorithm is proposed in this paper lies with this idea. The fuzzy academic set is generated from the student’s academic data. This is analyzed by the fuzzy-matrix set. The prediction academic data is referred as the management of data or data mining. Data mining is the science of analyzing the data for obtaining more information than Keywords the current information. The hidden information appears by this technique.

Computational intelligence based lossless regeneration (CILR) of blocked gingivitis intraoral image transportation Anirban Bhowmik; Joydeep Dey; Arindam Sarkar; Sunil Karforma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

This paper presented that an intraoral image has been wrapped during wireless transportation with an encryption tool with an added essence of lossless regeneration property. Threshold based cryptographic transportation has provided the construction of reliable and robust medical data communication system. The accumulation of threshold shares only would result to the formation of the intraoral gingivitis image at the receivers’ end. The proposed technique dealt with the generation of n number of partial shares by creating a unique frame structure by the dentist / physician. Additional feature has been proposed on the computational lossless transportation.The existing techniques cause a high computational complexity. The proposed technique ensured the lossless regeneration property while blocked gingivitis image sharing. Filling of bits have been incorporated to ensure the static sized homogeneous blocks of intraoral gingivitis image. A graphical masking method had been deployed, followed by successive decryption procedure on minimum threshold shares that ensure lossless data regeneration. This can guide the dental treatment with enhanced accuracy. Different types of statistical testing like entropy analysis and histogram analysis confirms the exhibition of authenticity, confidentiality, and integrity of our proposed technique.

Extractive Based Single Document Text Summarization Using Clustering Approach Pankaj Kailas Bhole; A. J. Agrawal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 2: June 2014
Publisher : Institute of Advanced Engineering and Science

Text summarization is an old challenge in text mining but in dire need of researcher’s attention in the areas of computational intelligence, machine learning and natural language processing. We extract a set of features from each sentence that helps identify its importance in the document. Every time reading full text is time consuming. Clustering approach is useful to decide which type of data present in document. In this paper we introduce the concept of k-mean clustering for natural language processing of text for word matching and in order to extract meaningful information from large set of offline documents, data mining document clustering algorithm are adopted.

Rice grain classification using multi-class support vector machine (SVM) Shafaf Ibrahim; Nurul Amirah Zulkifli; Nurbaity Sabri; Anis Amilah Shari; Mohd Rahmat Mohd Noordin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.

Implementation of Business Intelligence For Sales Management Bouzekri Moustaid; Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

Today's company operates in a socio-economic environment increasingly demanding. In such a context, it is obliged to adopt a competitive approach by exploiting at best the information that it possesses for developing appropriate action plans and taking effective decisions. The decision support systems provide to the enterprise the tools that help it for decision-making based on techniques and methodologies coming from domain of applied mathematics such as optimization, statistics and theory of the decision. The decision support systems are composed of various components such as data warehouses, ETL tools and reporting and analysis tools.

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OAI Link

Editorial Team

Contact

Reviewer

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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