IAES International Journal of Artificial Intelligence (IJ-AI)
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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<![CDATA[Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow ]]>
<![CDATA[Wan Nur Hawa Fatihah Wan Zurey]]>;
<![CDATA[Shuhaida Ismail]]>;
<![CDATA[Aida Mustapha]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp464-472
<![CDATA[Estimating the reliability of potential prediction is very crucial as our life depended heavily on it. Thus, a simulation that concerned hydrological factors such as streamflow must be enhanced. In this study, Autoregressive (AR) and Artificial Neural Networks (ANN) were used. The forecasting result for each model was assessed by using various performance measurements such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE) and Nash-Sutcliffe Model Efficiency Coefficient (CE). The experimental results showed the forecast performance of Durian Tunggal reservoir datasets by using ANN Model 7 with 7 hidden neurons has better forecast performance compared to AR (4). The ANN model has the smallest MAE (0.0116 m3/s), RMSE (0.0607 m3/s), MAPE (1.8214% m3/s), MFE (0.0058 m3/s) and largest CE (0.9957 m3/s) which show the capability of fitting to a nonlinear dataset. In conclusion, high predictive precision is an advantage as a proactive or precautionary measure that can be inferred in advance in order to avoid certain negative effects.]]>
<![CDATA[Machine learning-based technique for big data sentiments extraction ]]>
<![CDATA[Noraini Seman]]>;
<![CDATA[Nurul Atiqah Razmi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp473-479
<![CDATA[A huge amount of data is generated every minute for social networking and content sharing via Social media sites that can be in a form of structured, unstructured or semi-structured data. One of the largest used social media sites is Twitter, where each and every day millions of data generated in the form of unstructured tweets. Tweets or opinions of the people can be used to extract sentiments of the people. Sentiment analysis is beneficial for organizations to improve their products and make required changes on demand to increase their profit. In this paper, three machine learning algorithms Support Vector Machine (SVM), Decision Trees (DT), and Naive Bayes (NB) for classifying sentiments of twitters data. The purpose of this research is to compare the outcomes of these algorithms to identify best machine learning method which gives most accurate and efficient results for classifying twitter data. Our experimental result shows that same preprocessing methods on a different dataset affect similarly the classifiers performance. After analyzing the results it is observed that SVM provides 64.96%, 71.26% and 91.25% precision which is better than other two algorithms. Also, overall Recall and F-measure rate of SVM is greater than NB and DT for three datasets. However, it is important to further study current available preprocessing techniques that help us to improve results of various classifiers.]]>
<![CDATA[Region of interest-based image retrieval techniques: a review ]]>
<![CDATA[Mardhiyah Md Jan]]>;
<![CDATA[Nasharuddin Zainal]]>;
<![CDATA[Shahrizan Jamaludin]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp520-528
<![CDATA[This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.]]>
<![CDATA[Comparison of daily rainfall forecasting using multilayer perceptron neural network model ]]>
<![CDATA[Mazwin Arleena Masngut]]>;
<![CDATA[Shuhaida Ismail]]>;
<![CDATA[Aida Mustapha]]>;
<![CDATA[Suhaila Mohd Yasin]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp456-463
<![CDATA[Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values.]]>
<![CDATA[Long-term load forecasting using grey wolf optimizer -least-squares support vector machine ]]>
<![CDATA[Z. M. Yasin]]>;
<![CDATA[N. A. Salim]]>;
<![CDATA[N.F.A. Aziz]]>;
<![CDATA[Y.M. Ali]]>;
<![CDATA[H. Mohamad]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp417-423
<![CDATA[Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-LSSVM. Mean Absolute Percentage Error (MAPE) is used as the quantify measurement of the prediction model. The objective of the optimization is to minimize the value of MAPE. The performance of GWO-LSSVM is compared with other methods such as LSSVM and Ant Lion Optimizer – Least-Square Support Vector Machine (ALO-LSSVM). From the results obtained, it can be concluded that GWO-LSSVM provide lower MAPE value which is 0.13% as compared to other methods.]]>
<![CDATA[Pedestrian detection using Doppler radar and LSTM neural network ]]>
<![CDATA[Mussyazwann Azizi Mustafa Azizi]]>;
<![CDATA[Mohammad Nazrin Mohd Noh]]>;
<![CDATA[Idnin Pasya]]>;
<![CDATA[Ahmad Ihsan Mohd Yassin]]>;
<![CDATA[Megat Syahirul Amin Megat Ali]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp394-401
<![CDATA[Integration of radar systems as primary sensor with deep learning algorithms in driver assist systems is still limited. Its implementation would greatly help in continuous monitoring of visual blind spots from incoming pedestrians. Hence, this study proposes a single-input single-output based Doppler radar and long short-term memory (LSTM) neural network for pedestrian detection. The radar is placed in monostatic configuration at an angle of 45 degree from line of sight. Continuous wave with frequency of 1.9 GHz are continuously transmitted from the antenna. The returning signal from the approaching subjects is characterized by the branching peaks higher than the transmitted frequency. A total of 1108 spectrum traces with Doppler shifts characteristics is acquired from eight volunteers. Another 1108 spectrum traces without Doppler shifts are used for control purposes. The traces are then fed to LSTM neural network for training, validation and testing. Generally, the proposed method was able to detect pedestrian with 88.9% accuracy for training and 87.3% accuracy for testing.]]>
<![CDATA[Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization ]]>
<![CDATA[Sulaiman bin Sabikan]]>;
<![CDATA[Nawawi. S. W]]>;
<![CDATA[NAA Aziz]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp488-496
<![CDATA[A method for the development of Time-to-Collision (TTC) mathematical model for outdoor Unmanned Aerial Vehicle (UAV) using Particles Swarm Optimization (PSO), are presented. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking control system when the throttle is fully released. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their parameter, for instance, current speed and payload. However, this paper focus on the methodology of the implementation of PSO to develop the TTC model for 5 different set of payloads. This work utilizes a quadcopter as our testbed system, that equipped with a Global Positioning System (GPS) receiver unit, a flight controller with data recording capability and ground control station for real-time monitoring. The recorded onboard flight mission data for 5 different set of payloads has been analyzed to develop a mathematical model of TTC through the PSO approach. The horizontal ground speed, throttle magnitudes and flight time stamp are extracted from the on-board quadcopter flight mission. PSO algorithm is used to find the optimal linear TTC model function, while the mean square error is used to evaluate the best fitness of the solution. The results of the TTC mathematical model for each payload are described.]]>
<![CDATA[Optimization of detection of single line to ground fault by controlling peterson coil through ANFIS ]]>
<![CDATA[Feryal Ibrahim Jabbar]]>;
<![CDATA[Dur Muhammad Soomro]]>;
<![CDATA[Adnan Hasan Tawafan]]>;
<![CDATA[Mohd Noor bin Abdullah]]>;
<![CDATA[Nur Hanis binti Mohammad Radzi]]>;
<![CDATA[Mazhar Hussain Baloch]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp409-416
<![CDATA[The most common fault in the distribution network is the single line to ground fault (SLGF). With earthling in the distribution network, it causes electrical arc as well as a high voltage in the faulted phase compared to other two healthy phases. It increases the danger of separation and isolation in the power network. One of the classical technique to control the arc is through Peterson Coil (PC), which detects and turns off/reduces the electrical arc making the network safer, increasing its reliability and device's safety. To control the PC, some of the techniques used in this research area are PID, FL, NN etc. This paper presents Adaptive Neural-Fuzzy Inference System (ANFIS) technique to controlling the PC. It gives the best results by detecting the fault, reducing the electrical arc and minimizing the fault current to the rated current in a very short time. Moreover, this research focuses on suppressing fault current by looking at its higher and lower peaks. Also, it calculates the opposing inductance to compensate the capacitance caused. It will save thousands of tons of copper costs. This research was conducted using MATLAB. For the validity of the proposed technique results, PID control technique was used.]]>
<![CDATA[CLUSTER PREDICTION MODEL FOR MARKET BASKET ANALYSIS: QUEST FOR BETTER ALTERNATIVES TO ASSOCIATIVE RULE MINING APPROACH ]]>
<![CDATA[Ojugo, Arnold Adimabua]]>;
<![CDATA[Eboka, Andrew Okonji]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp%p
<![CDATA[Market basket analysis seeks to apply association rule mining on the massive sales transaction data. It yields an outcome that either aims to suppress product stock-up unnecessarily and/or product being stock-out. Such decision support system seeks to avoid the unnecessary demurrage and help businesses to keep their customers via better decision and improved service. Market data are time-bound on supply-demand value chain. With customer behavior varying in time, we seek to predict purchase of commonly combined itemset for a next period ? so that businesses can better support their decisions via adequate provisions of the required inventory. We use 3-KDD dataset and Delta Mall dataset ? adapting a time-clustering algorithm that examines buying behavior of customers, their preferences and frequency with which goods are purchased in common as a basket. Model yields average 162-rules for four-dataset from dataset. Result shows that previous basket items by random customers allow the selection purchase of items of similar value as best combined due to its shelf-placement using the concept of feature drift.]]>
<![CDATA[Local search algorithms based on benchmark test functions problem ]]>
<![CDATA[Atheer Bassel]]>;
<![CDATA[Hussein M. Haglan]]>;
<![CDATA[Akeel Sh. Mahmoud]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v9.i3.pp529-534
<![CDATA[Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.]]>
