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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.
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Articles 50 Documents
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Search results for , issue "Vol 12, No 2: June 2023" : 50 Documents clear
An investigation of wine quality testing using machine learning techniques Sathishkumar Mani; Reshmy Avanavalappil Krishnankutty; Sabaria Swaminathan; Prasannavenkatesan Theerthagiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp747-754

Abstract

Quality is the most determining factor for any product. Optimal care and best measures are to be taken in assessing the quality of any product. This work deals with determining the quality of wine using intelligence-based learning techniques. In order to estimate the quality of wine, several experiments are performed on wine datasets. The main purpose of our work is to study and discover an efficient machine learning (ML) model that could determine the quality of wine given some Physico-chemical features. This study establishes that selecting important features to evaluate rather than all of them can lead to improved forecasts. According to the results, this approach may provide people who are not wine experts a greater opportunity to choose a fine wine.
A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques Hicham Benradi; Ahmed Chater; Abdelali Lasfar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp627-640

Abstract

Facial recognition technology has been used in many fields such as security, biometric identification, robotics, video surveillance, health, and commerce due to its ease of implementation and minimal data processing time. However, this technology is influenced by the presence of variations such as pose, lighting, or occlusion. In this paper, we propose a new approach to improve the accuracy rate of face recognition in the presence of variation or occlusion, by combining feature extraction with a histogram of oriented gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the Canny contour detector techniques, as well as a convolutional neural network (CNN) architecture, tested with several combinations of the activation function used (Softmax and Segmoïd) and the optimization algorithm used during training (adam, Adamax, RMSprop, and stochastic gradient descent (SGD)). For this, a preprocessing was performed on two databases of our database of faces (ORL) and Sheffield faces used, then we perform a feature extraction operation with the mentioned techniques and then pass them to our used CNN architecture. The results of our simulations show a high performance of the SIFT+CNN combination, in the case of the presence of variations with an accuracy rate up to 100%.
Hypertension prediction using machine learning algorithm among Indonesian adults Rico Kurniawan; Budi Utomo; Kemal N. Siregar; Kalamullah Ramli; Besral Besral; Ruddy J. Suhatril; Okky Assetya Pratiwi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp776-784

Abstract

Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.
Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis Hana Jamali; Younes Chihab; Iván García-Magariño; Omar Bencharef
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp892-911

Abstract

Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.
Masking preprocessing in transfer learning for damage building detection Hapnes Toba; Hendra Bunyamin; Juan Elisha Widyaya; Christian Wibisono; Lucky Surya Haryadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp552-559

Abstract

The sudden climate change occurring in different places in the world has made disasters more unpredictable than before. In addition, responses are often late due to manual processes that have to be performed by experts. Consequently, major advances in computer vision (CV) have prompted researchers to develop smart models to help these experts. We need a strong image representation model, but at the same time, we also need to prepare for a deep learning environment at a low cost. This research attempts to develop transfer learning models using low-cost masking pre-processing in the experimental building damage (xBD) dataset, a large-scale dataset for advancing building damage assessment. The dataset includes eight types of disasters located in fifteen different countries and spans thousands of square kilometers of satellite images. The models are based on U-Net, i.e., AlexNet, visual geometry group (VGG)-16, and ResNet-34. Our experiments show that ResNet-34 is the best with an F1 score of 71.93%, and an intersection over union (IoU) of 66.72%. The models are built on a resolution of 1,024 pixels and use only first-tier images compared to the state-of-the-art baseline. For future orientations, we believe that the approach we propose could be beneficial to improve the efficiency of deep learning training.
Insights on assessing image processing approaches towards health status of plant leaf using machine learning Harsha Raju; Veena Kalludi Narasimhaiah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp884-891

Abstract

With the advancement of digital image processing in agriculture and crop cultivation, imaging techniques are adopted to acquire real-time health status. Out of all the parts of plants, the leaf is the direct indicator of its health status, and hence applying various image processing approaches could benefit the process of yielding informative cases of plant health. At present, there are various approaches, e.g., feature extraction, segmentation, identification, the classification being evolved up with more dependencies being found in using machine learning; the studies show many contributions towards this challenge. However, it is not yet conclusive to understand the optimal approach. Hence, this paper highlights an explicit strength and weakness associated with the existing approaches existing imaging processing techniques to identify the disease condition from an input of plant leaves' image. The study also contributes to highlighting open-end research problems to have conclusive remarks about effectiveness. 
Cuckoo search algorithm for construction site layout planning Meilinda Fitriani Nur Maghfiroh; Anak Agung Ngurah Perwira Redi; Janice Ong; Muhamad Rausyan Fikri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp851-860

Abstract

A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature-inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.
An improved artificial bee colony with perturbation operators in scout bees’ phase for solving vehicle routing problem with time windows Salah Mortada; Yuhanis Yusof
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp656-666

Abstract

An example of a combinatorial problem is the vehicle routing problem with time windows (VRPTW), which focuses on choosing routes for a limited number of vehicles to serve a group of customers in a restricted period. Meta-heuristics algorithms are successful techniques for VRPTW, and in this study, existing modified artificial bee colony (MABC) algorithm is revised to provide an improved solution. One of the drawbacks of the MABC algorithm is its inability to execute wide exploration. A new solution that is produced randomly and being swapped with best solution when the previous solution can no longer be improved is prone to be trapped in local optima. Hence, this study proposes a perturbed MABC known as pertubated (P-MABC) that addresses the problem of local optima. P-MABC deploys five types of perturbation operators where it improvises abandoned solutions by changing customers in the solution. Experimental results show that the proposed P-MABC algorithm requires fewer number of vehicles and least amount of travelled distance compared with MABC. The P-MABC algorithm can be used to improve the search process of other population algorithms and can be applied in solving VRPTW in domain applications such as food distribution.
An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income Nurfarawahida Ramly; Mohd Saifullah Rusiman; Muhammad Ammar Shafi; Suparman .S; Firdaus Mohamad Hamzah; Ozlem Gurunlu Alma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp543-551

Abstract

Regression analysis is a popular tool used in data analysis, whereas fuzzy regression is usually used for analyzing uncertain and imprecise data. In the industrial area, the company usually has problems in predicting the future manufacturing income. Therefore, a new approach model is needed to solve the future company prediction income. This article analyzed the manufacturing income by using the multiple linear regression (MLR) model and fuzzy linear regression (FLR) model proposed by Tanaka and Zolfaghari, involving 9 explanatory variables. In order to find the optimum of the FLR model, the degree of fitting (H) was adjusted between 0 to 1. The performance of three methods has been measured by using mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The analysis proved that FLR with Zolfaghari’s model with the degree of fitting of 0.025 outperformed the MLR and FLR with Tanaka’s model with the smallest error value. In conclusion, the manufacturing income is directly proportional to 6 independent variables. Furthermore, the manufacturing income is inversely proportional to 3 independent variables. This model is suitable in predicting future manufacturing income.
Location-aware hybrid microscopic routing scheme for mobile opportunistic network Shobha R. Bharamagoudar; Shivakumar V. Saboji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp785-793

Abstract

Mobile opportunistic networks (MON) has been used for provisioning delay-tolerant applications. In MON the device communicates with each other with no assured end-to-end paths from source and destination because of frequent topology changes, node mobility, low density, and intermittent connectivity. In MON the device battery drains very fast for performing activities such as scanning, transceiver, and other computational processes, impacting the overall performance thus, designing energy-efficient routing is a challenging task. The routing employs a store-carry-and-forward mechanism for packet communication, where the packet is composed of time-to-live (TTL) and is kept in buffer till the opportunity arises. In improving delivery ratio message replication has been adopted; however, induces high network congestion. Here we present a location-aware hybrid microscopic routing (LAHMR) scheme for MON. The LAHMR provides an effective packet transmission scheme with location awareness and high reliability by limiting unnecessary packets being circulated in the network. Experiment outcome shows the LAHMR scheme achieves a much better delivery ratio with less delay, and also reduces the number of a forwarder for transmitting a packet, aiding in the reduction of network overhead concerning recent routing method namely the social-aware reliable forwarding (SCARF) technique.

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