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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

GS-OPT: A new fast stochastic algorithm for solving the non-convex optimization problem Xuan Bui; Nhung Duong; Trung Hoang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Non-convex optimization has an important role in machine learning. However, the theoretical understanding of non-convex optimization remained rather limited. Studying efficient algorithms for non-convex optimization has attracted a great deal of attention from many researchers around the world but these problems are usually NP-hard to solve. In this paper, we have proposed a new algorithm namely GS-OPT (General Stochastic OPTimization) which is effective for solving the non-convex problems. Our idea is to combine two stochastic bounds of the objective function where they are made by a commonly discrete probability distribution namely Bernoulli. We consider GS-OPT carefully on both the theoretical and experimental aspects. We also apply GS-OPT for solving the posterior inference problem in the latent Dirichlet allocation. Empirical results show that our approach is often more efficient than previous ones.

A general framework of genetic multi-agent routing protocol for improving the performance of MANET environment Mustafa Hamid Hassan; Mohammed Ahmed Jubair; Salama A. Mostafa; Hazalila Kamaludin; Aida Mustapha; Mohd Farhan Md. Fudzee; Hairulnizam Mahdin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

These days, the fields of Mobile Ad hoc Network (MANET) have provided increasing prevalence and consequently, MANET is now a subject of considerable significance for the researchers to instigate research activities. MANET is the collaborative commitment of an assemblage of portable (or mobile) hubs (or nodes) without the necessary mediation of any unified (or centralized) gateway (or access point) or existent framework. There exists a growing inclination or course to embrace MANET for business utilization. MANET is a rising domain of research to give different services in communication to end-clients or consumers. However, these communication services of MANET utilize a large amount of transfer speed (or bandwidth) and a huge measure of web speed. Bandwidth optimization is essential in different information interchanges for fruitful acknowledgement and the application of such a technological innovation. This paper integrates the Genetic Algorithm (GA) and the Multi-Agent System (MAS) to improve the QoS requirements. The proposed framework called Genetic Multi-Agent Routing Protocol (GMARP). The aims of the proposed framework are to utilize the benefits of both approaches in order to fulfil QoS such as (delay, bandwidth, and the number of hops) in the different types of routing conventions (or protocols) such as being (proactive and reactive). In this paper is a simulation scenario to demonstrate the ability of the proposed framework to be satisfied with QoS requirements.

STA/LTA trigger algorithm implementation on a seismological dataset using Hadoop MapReduce Youness Choubik; Abdelhak Mahmoudi; Mohammed Majid Himmi; Lahcen El Moudnib
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

In this work we implemented STA/LTA trigger algorithm, which is widely used in seismic detection, using Hadoop MapReduce. Thisimplementation allows to ﬁnd out how effective it is in this type of tasks as well as to accelerate the detection process by reducing the processing time. We tested our implementation on a seismological dataset of 14 broadband seismic stations and compare it with the traditional one. The results show that MapReduce decreased the processing time by 34% compared to the traditional implementation.

A deep learning AlexNet model for classification of red blood cells in sickle cell anemia Hajara Aliyu Abdulkarim; Mohd Azhar Abdul Razak; Rubita Sudirman; Norhafizah Ramli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.

Features detection based blind handover using kullback leibler distance for 5G HetNets systems Adnane El Hanjri; Aawatif Hayar; Abdelkrim Haqiq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

The Fifth Generation of Mobile Networks (5G) is changing the cellular network infrastructure paradigm, and Small Cells are a key piece of this shift. But the high number of Small Cells and their low coverage involve more Handovers to provide continuous connectivity, and the selection, quickly and at low energy cost, of the appropriate one in the vicinity of thousands is also a key problem. In this paper, we propose a new method, to have an efficient, blind and rapid handover just by analysing Received Signal probability density function instead of demodulating and analysing Received Signal itself as in classical handover. The proposed method exploits KL Distance, Akaike Information Criterion (AIC) and Akaike weights, in order to decide blindly the best handover and the best Base Station (BS) for each user

Slope stability prediction of road embankment on soft ground treated with prefabricated vertical drains using artificial neural network Rufaizal Che Mamat; Abd Manan Samad; Anuar Kasa; Siti Fatin Mohd Razali; Azuin Ramli; Mohd Badrul Hafiz Che Omar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents the slope stability for road embankment constructed on the soft ground treated with prefabricated vertical drains (PVDs). The slope stability was evaluated based on the factor of safety (FOS) through numerical analysis and modeled with an artificial neural network (ANN). The permeability ratio of the smear effect was verified based on a comparative analysis between field data and numerical simulation to develop the datasets used in ANN model training. A total of 75 datasets generated from numerical simulations were randomly selected into three groups for training, testing, and validation. The coefficient of determination (R2) and root mean square error (RMSE) were considered to evaluate the performance ANN model. It was found that the developed ANN model showed strong potential for predicting slope stability within the accepted range.

Evaluation of particle swarm optimization for strength determination of tropical wood polymer composite Marina Yusoff; Alya Nurizzati Mohd Basir; Norhidayah A Kadir; Shahril Anuar Bahari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

A wood-polymer composite (WPCs) refers to wood-based components that are coupled with polymers to produce a composite material. Obtaining the best strength for the tropical WPCs is still a lack of research mainly for the tropical timber species and require a large consumption of time and cost. This paper highlighted the evaluation of particle swarm optimization (PSO) to assist in finding the optimal value of the composition of tropical WPCs to obtain the best strength that would offer a betterment in a quality product of WPCs. The findings demonstrate that PSO has been shown as a viable and efficient method for WPCs strength. The composition of Sentang, wood sawdust of 50%, HDPE of 49% and 1% coupling agent is demonstrated the best strength for the WPC. The employment of PSO as an assisted tool would give significant benefit to the manufacturer and researcher to determine the composition of material with less cost and time.

Supervised attention for answer selection in community question answering Thanh Thi Ha; Atsuhiro Takasu; Thanh Chinh Nguyen; Kiem Hieu Nguyen; Van Nha Nguyen; Kim Anh Nguyen; Son Giang Tran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores matchLSTM for answer selection in CQA. A lexical gap in CQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into matchLSTM. Specifically, we leverage lexical-semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.

Training configuration analysis of a convolutional neural network object tracker for night surveillance application Zulaikha Kadim; Mohd Asyraf Zulkifley; Nor Azwan Mohamed Kamari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Automated surveillance during the night is important as it is the period when crimes usually happened. By providing continuous monitoring, coupled with a real-time alert system, appropriate action can be taken immediately if a crime is detected. However, low lighting conditions during the night can degrade the quality of surveillance videos, where the captured images will have low contrast and less discriminative features. Consequently, these factors contribute to the problem of bad appearance representation of the object of interest in the tracking algorithm. Thus, a convolutional neural network-based object tracker for night surveillance is proposed by exploiting the deep feature strength in representing object features spatially and semantically. The proposed convolutional network consists of six layers that consist of three convolutional neural networks (CNN) and three fully connected (FC) layers. The network will be trained by using a binary classifier approach of objects and its background classes, which is updated on a fixed interval so that it fully encapsulates the changes in object appearance as it moves in the scene. The algorithm has been tested with different sets of training data configurations to find the best optimum ones with regards to VOT2015 evaluation protocols, tested on 14-night surveillance videos. The results show that the configuration of a total of 250 training samples with a sample ratio of 4:1 between positive and negative data delivers the best performance for the sequence length of [1,550]. It can be inferred that more information on the object is required compared to the background, where the background might be homogeneous due to low lighting conditions. In conclusion, this algorithm is suitable to be implemented for night surveillance application.

Index-based transmission for distributed generation in voltage stability and loss control incorporating optimization technique Fareed Danial Ahmad Kahar; Ismail Musirin; Muhamad Faliq Mohamad Nazer; Shahrizal Jelani; Mohd Helmi Mansor
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

The integration of Distributed Generation (DG) in a distribution network may significantly affect distribution performance. With the penetration of DG, voltage security is no longer an issue in the transmission network. This paper presents a study of Distributed Generation on the IEEE 26-Bus Reliability Test System (RTS) with the use of Fast Voltage Stability Index (FVSI) for determining its location and incorporated with Grasshopper Optimization Algorithm (GOA) to optimize the sizing of the DG. The study emphasizes the power loss of the system in which a comparison between Evolutionary Programming (EP) and Grasshopper Optimization Algorithm is done to determine which optimization technique gives an optimal result for the DG solution. The results show that the proposed algorithm is able to provide a slightly better result compared to EP.

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Imam Much Ibnu Subroto

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ijai@iaesjournal.com

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Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

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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