Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

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

22 23 24 25 26

Classification of tomato leaf diseases using MobileNet v2 Siti Zulaikha Muhammad Zaki; Mohd Asyraf Zulkifley; Marzuraikah Mohd Stofa; Nor Azwan Mohammed Kamari; Nur Ayuni Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.

A review on neural networks approach on classifying cancers Maha Mahmood; Belal Al-Khateeb; Wisam Makki Alwash
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Cancer is a dreadful disease. Millions of people died every year because of this disease. Neural networks are currently a burning research area in medical scienc It is very essential for medical practitioners to opt a proper treatment for cancer patients. Therefore, cancer cells should be identified correctly. Current developments in biological as well as in the computer science encouraged more studies to examine the role related to computational techniques in broad sphere regarding certain researches related to cancer. Using different AI approaches with regard to the disease’s medical diagnosis has been more general in recent times. Furthermore, there is more concentration on shown advantages of machine learning and AI methods. Cancer can be considered as one of the terrible diseases. Yearly, a lot of humans are dying from cancer. It is very essential for the practitioners of medical field to use suitable treatment regarding patients experiencing cancer. The data on cancer is specified as collection regarding thousands of genes. Thus, the cells of cancer must be properly detected. Currently, neural networks are considered as very significant area of research in the medical science, particularly in urology, radiology, cardiology, oncology, and a lot more. The presented work will survey different techniques of neural networks to classify lymph, neck and head, as well as breast cancer. The major goal of this work in the medical diagnostics has been guiding a lot of studies for developing user-friendly as well as inexpensive techniques, processes, as well as systems for the clinicians.

Enumeration of the minimal node cutsets based on necessary minimal paths Yasser Lamalem; Khalid Housni; Samir Mbarki
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Reliability evaluation is an important research field for a complex network. The most popular methods for such evaluation often use Minimal Cuts (MC) or Minimal paths (MP). Nonetheless, few algorithms address the issue of the enumeration of all minimal cut sets from the source node s to the terminal node t when only the nodes of the network are subject to random failures. This paper presents an effective algorithm which enumerates all minimal node cuts sets of a network. The proposed algorithm runs in two steps: The first one is used to generate a subset of paths, called necessary minimal paths, instead of all minimal paths. Whereas, the second step stands to build all minimal cutsets from the necessary minimal paths.

Biogeography optimization algorithm based next web page prediction using weblog and web content features Roshan Anant Gangurde; Binod Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Recommendation of web page as per users’ interest is a broad and important area of research. Researcher adopts user behavior from actions present in cookies, logs and search queries. This paper has utilized a prior webpage fetching model using web page prediction. For this purpose, web content in form of text and weblog features are analyzed. As per dynamic user behavior, proposed model LWPP-BOA (Logistic Web Page Prediction By Biogeography Optimization Algorithm) predict page by using genetic algorithm. Based on user actions, weblog feature are developed in form of association rules, while web content gives a set of relevant text patterns. Page prediction as per random user behavior is enhanced by means of Biogeography Optimization Algorithm where crossover operation is performed as per immigration and emigration values. Here population updation depends on other parameters of chromosome except fitness value. Experiments are conducted on real dataset having web content and weblogs. Results are compared using precision, coverage, M-Metric, MAE and RMSE parameters and it indicates that the proposed work is better than other approaches already in use.

Continuous domain ant colony optimization for distributed generation placement and losses minimization Zulkiffli Abdul Hamid; Ismail Musirin; Ammar Yasier Azman; Muhammad Murtadha Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

This paper proposes a method for distributed generation (DG) placement in distribution system for losses minimization and voltage profile improvement. An IEEE 33-bus radial distribution system is used as the test system for the placement of DG. To facilitate the sizing of DG capacity, a meta-heuristic algorithm known as Continuous Domain Ant Colony Optimization (ACOR) is implemented. The ACOR is a modified version of the traditional ACO which was developed specially for solving continuous domain optimization problem like sizing a set of variables. The objective of this paper is to determine the optimal size and location of DG for power loss minimization and voltage profile mitigation. Three case studies were conducted for the purpose of verification. It was observed that the proposed technique is able to give satisfactory results of real power loss and voltage profile at post-optimization condition. Experiment under various loadings of the test system further justifies the objective of the study.

Adaptive neuro-fuzzy inference system based evolving fault locator for double circuit transmission lines A Naresh Kumar; P Sridhar; T Anil Kumar; T Ravi Babu; V Chandra Jagan Mohan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Evolving faults are starting in one phase of circuit and spreading to other phases after some time. There has not been a suitable method for locating evolving faults in double circuit transmission line until now. In this paper, a novel method for locating different types of evolving faults occurring in double circuit transmission line is proposed by considering adaptive neuro-fuzzy inference system. The fundamental current and voltage magnitudes are specified as inputs to the proposed method. The simulation results using MATLAB verify the effectiveness and correctness of the protection method. Simulation results show the robustness of the method against different fault locations, resistances, time intervals, and all evolving fault types. Moreover, the proposed method yields satisfactory performance against percentage errors and fault location line parameters. The proposed method is easy to implement and cost-effective for new and existing double circuit transmission line installation

Conflicting opinions in connection with digital superintelligence Ahmed Al-Imam; Marek A. Motyka; Mariusz Z. Jędrzejko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

In 1964, Nikolai Kardashev proposed the Kardashev scale, a system for measuring the extent of technological advancement of a civilization based on the magnitude of energy consumption. We are approaching an inevitable type-1 civilization, and artificial superintelligence superior to that of humans can concur with a higher-hierarchy Kardashev civilization. We aim to survey public opinions, specifically video gamers, worldwide compared to those in Poland, concerning artificial general intelligence and superintelligence. We implemented an amalgam of cross-sectional and longitudinal analyses of the database of literature and Google search engine. The geographic mapping of surface web users who are interested in artificial superintelligence revealed the top ten contributing countries: Iran, Mexico, Colombia, Brazil, India, Peru, South Africa, Romania, Switzerland, and Chile. Developing countries accounted for 54.84% of the total map. Polish people were less enthusiastic about artificial general intelligence and superintelligence compared with the rest of the world. Futuristic technological innovations imply an acceleration in artificial intelligence and superintelligence. This scenario can be pessimistic, as superintelligence can render human-based activities obsolete. However, integrating artificial intelligence with humans, via brain-computer interface technologies, can be protective. Nonetheless, legislation in connection with information technologies is mandatory to regulate upcoming digital knowledge and superintelligence.

Comparison of CNNs and SVM for voice control wheelchair Mohammad Shahrul Izham Sharifuddin; Sharifalillah Nordin; Azliza Mohd Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+ to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.

Sentiment analysis of informal Malay tweets with deep learning Ong Jun Ying; Muhammad Mun'im Ahmad Zabidi; Norhafizah Ramli; Usman Ullah Sheikh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Twitter is an online microblogging and social-networking platform which allows users to write short messages called tweets. It has over 330 million registered users generating nearly 250 million tweets per day. As Malay is the national language in Malaysia, there is a significant number of users tweeting in Malay. Tweets have a maximum length of 140 characters which forces users to stay focused on the message they wish to disseminate. This characteristic makes tweets an interesting subject for sentiment analysis. Sentiment analysis is a natural language processing (NLP) task of classifying whether a tweet has a positive or negative sentiment. Tweets in Malay are chosen in this study as limited research has been done on this language. In this work, sentiment analysis applied to Malay tweets using the deep learning model. We achieved 77.59% accuracy which exceeds similar work done on Bahasa Indonesia.

Optimal economic dispatch of power generation solution using lightning search algorithm Murad Yahya Nassar; Mohd Noor Abdullah; Asif Ahmed Rahimoon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Economic dispatch (ED) is the power demand allocating process for the committed units at minimum generation cost while satisfying system and operational constraints. Increasing cost of fuel price and electricity demand can increase the cost of thermal power generation. Therefore, robust and efficient optimization algorithm is required to determine the optimal solution for ED problem in power system operation and planning. In this paper the lightning search algorithm (LSA) is proposed to solve the ED problem. The system constraints such as power balance, generator limits, system transmission losses and valve-points effects (VPE) are considered in this paper. To verify the effectiveness of LSA in terms of convergence characteristic, robustness, simulation time and solution quality, the two case studies consists of 6 and 13 units have been tested. The simulation results show that the LSA can provide optimal cost than many methods reported in literature. Therefore, it has potential to solve many optimization problems in power dispatch and power system applications.

Page 24 of 173 | Total Record : 1722

22 23 24 25 26

Filter by Year

2012 2025

Filter By Issues

All Issue Vol 14, No 6: December 2025 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

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