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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 24 Documents

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Search results for , issue "Vol 9, No 2: June 2020" : 24 Documents clear

OPF for large scale power system using ant lion optimization: a case study of the Algerian electrical network Ramzi Kouadri; Ismail Musirin; Linda Slimani; Tarek Bouktir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents a study of the optimal power flow (OPF) for a large scale power system. A metaheuristic search method based on the Ant Lion Optimizer (ALO) algorithm is presented and has been confirmed in the real and larger scale Algerian 114-bus system for the OPF problem with and without static VAR compensator (SVC) devices. To get the highest impact of SVC devices in terms of improving the voltage profile, minimize the total generation cost and reduction of active power losses, the ALO algorithm was applied to determine the optimal allocation of SVC devices. The results obtained by the ALO method were compared with other methods in the literature such as DE, GA-ED-PS, QP, and MOALO, to see the efficiency of the proposed method. The proposed method has been tested on the Algerian 114-bus system with objective functions is the minimization of total generation cost (TGC) with two different vectors of variables control.

Vietnamese handwritten character recognition using convolutional neural network Truong Quang Vinh; Le Hoai Duy; Nguyen Thanh Nhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Handwriting recognition is one of the core applications of computer vision for real-word problems and it has been gaining more interest because of the progression in this field. This paper presents an efficient model for Vietnamese handwriting character recognition by Convolutional Neural Networks (CNNs) – a kind of deep neural network model can achieve high performance on hard recognition tasks. The proposed architecture of the CNN network for Vietnamese handwriting character recognition consists of five hidden layers in which the first 3 layers are convolutional layers and the last 2 layers are fully-connected layers. Overfitting problem is also minimized by using dropout techniques with the reasonable drop rate. The experimental results show that our model achieves approximately 97% accuracy.

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.

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.

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.

A hybrid technique for single-source shortest path-based on A* algorithm and ant colony optimization Sameer Alani; Atheer Baseel; Mustafa Maad Hamdi; Sami Abduljabbar Rashid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in fast time processing. ACO will optimize this path and output the best path. The result showed that the proposed solution provides an average decreasing time performance is 13.5%.

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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

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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

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