cover
Article Per Year (5 Year)
All
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
IAES International Journal of Artificial Intelligence (IJ-AI)
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 5 Documents
 1 
Search results for , issue "Vol 5, No 1: March 2016" : 5 Documents clear
Twitter Tweet Classifier Ashwin V
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.942 KB) | DOI: 10.11591/ijai.v5.i1.pp41-44

Abstract

This paper addresses the task of building a classifier that would categorise tweets in Twitter. Microblogging nowadays has become a tool of communication for Internet users. They share opinion on different aspects of life. As the popularity of the microblogging sites increases the closer we get to the era of Information Explosion.Twitter is the second most used microblogging site which handles more than 500 million tweets tweeted everyday which translates to mind boggling 5,700 tweets per second. Despite the humongous usage of twitter there isn’t any specific classifier for these tweets that are tweeted on this site. This research attempts to segregate tweets and classify them to categories like Sports, News, Entertainment, Technology, Music, TV, Meme, etc. Naïve Bayes, a machine learning algorithm is used for building a classifier which classifies the tweets when trained with the twitter corpus. With this kind of classifier the user may simply skim the tweets without going through the tedious work of skimming the newsfeed.
Classification of Power Quality Events Using Wavelet Analysis and Probabilistic Neural Network Pampa Sinha; Sudipta Debath; Swapan Kumar Goswami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (859.4 KB) | DOI: 10.11591/ijai.v5.i1.pp1-12

Abstract

Power quality studies have become an important issue due to widespread use of sensitive electronic equipment in power system. The sources of power quality degradation must be investigated in order to improve the power quality. Switching transients in power systems is a concern in studies of equipment insulation coordination. In this paper a wavelet based neural network has been implemented to classify the transients due to capacitor switching, motor switching, faults, converter and transformer switching. The detail reactive powers for these five transients are determined and a model which uses the detail reactive power as the input to the Probabilistic neural network (PNN) is set up to classify the above mentioned transients. The simulation has been executed for an 11kv distribution system. With the help of neural network classifier, the transient signals are effectively classified.
Grading of Soybean Leaf Disease Based on Segmented Image Using K-means Clustering Sachin Balkrishna Jadhav; Sanjay B. Patil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1185.624 KB) | DOI: 10.11591/ijai.v5.i1.pp13-21

Abstract

Traditional method used for disease scoring scale to grade the plant diseases is mainly based on neckaed eye observation by agriculture expert or plant pathlogiest. In this method percentage scale was exclusively used to define different disease severities in an illustrated series of disease assessment keys for field crops.The assessment of plant leaf diseases using this aaproach which may be subjective, time consuming and cost effective.Also aacurate grading of leaf diseases is essential to the determination of pest control measures. In order to improve this process, here we propose a technique for automatically quantifying the damaged leaf area using k means clustering, which uses square Euclidian distances method for partition of leaf image.For grading of soybean leaf disese which appear on leaves based on segmented diseased region are done automatically by estiamting thae ratio of the unit pixel expressed under diseased region area and unit pixel expressed under Leaf region area.For experiment purpose samples of Bacterial Leaf Blight Septoria Brown spot, Bean Pod Mottle Virus infected soybean leaf images were taken for analysis.Finally estiamated diseased severity and its grading is compared with  manual scoring based on conventional illustrated key diagram was conducted. Comparative assessment results showed a good agreement between the numbers of percentage scale grading obtained by manual scoring and by image analysis The result shows that the proposed method is precise and reliable than visual evaluation performed by patahlogiest.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.148 KB) | DOI: 10.11591/ijai.v5.i1.pp22-34

Abstract

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.
Preventing Online Social Deception using Deception Matrix Alka Alka; Harjot Kaur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 1: March 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (326.779 KB) | DOI: 10.11591/ijai.v5.i1.pp35-40

Abstract

The organization collaboration is very important for the success of the organization. The persons who enter into the organization will interact with the other members of the organization. There exist leaders of the community who will be responsible for the management of the communication among the persons within the organization. Sometimes the information presented by the new person joining the community is not correct. That information will cause the deception over the network. In the purposed paper deception within the social media is going to be analyzed. Deception will cause legion of problems and sometimes death of the person who is deceived. The proposed paper suggests the mechanism for tackling such deceptions.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2016 2016


Reset
Filter By Issues
All Issue Vol 14, No 5: October 2025 Vol 14, No 4: August 2025 Vol 14, No 3: June 2025 Vol 14, No 2: April 2025 Vol 14, No 1: February 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue