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.
Articles
1,722 Documents
<![CDATA[Index-based transmission for distributed generation in voltage stability and loss control incorporating optimization technique ]]>
<![CDATA[Fareed Danial Ahmad Kahar]]>;
<![CDATA[Ismail Musirin]]>;
<![CDATA[Muhamad Faliq Mohamad Nazer]]>;
<![CDATA[Shahrizal Jelani]]>;
<![CDATA[Mohd Helmi Mansor]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (699.683 KB)
|
DOI: 10.11591/ijai.v9.i2.pp244-251
<![CDATA[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.]]>
<![CDATA[Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network ]]>
<![CDATA[Shafaf Ibrahim]]>;
<![CDATA[Saadi Bin Ahmad Kamaruddin]]>;
<![CDATA[Azlee Zabidi]]>;
<![CDATA[Nor Azura Md. Ghani]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v9.i4.pp616-622
<![CDATA[Rice is a staple food for 80% of the population in Southeast Asia. Thus, the quality control and classification of rice grain are crucial for more productive and sustainable production. This paper examines the contrastive analysis of rice grain classification performance between multi-class support vector machine (SVM) and artificial neural network (ANN). The analysis has been tested on three types of rice grain images which are Ponni, Basmati, and Brown rice. A digital image transformation analysis based on shape and color features was developed to classify the three types of rice grain. The performance of the proposed study is evaluated to 90 testing images of each rice variation. The ANN is observed to return higher classification accuracy at 93.34% using Level Sweep image transformation technique. Based on the results, it signifies that the ANN performs better classification than the multiclass SVM.]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
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[A modified correlation in principal component analysis for torrential rainfall patterns identification ]]>
<![CDATA[Shazlyn Milleana Shaharudin]]>;
<![CDATA[Norhaiza Ahmad]]>;
<![CDATA[Siti Mariana Che Mat Nor]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v9.i4.pp655-661
<![CDATA[This paper presents a modified correlation in principal component analysis (PCA) for selection number of clusters in identifying rainfall patterns. The approach of a clustering as guided by PCA is extensively employed in data with high dimension especially in identifying the spatial distribution patterns of daily torrential rainfall. Typically, a common method of identifying rainfall patterns for climatological investigation employed T mode-based Pearson correlation matrix to extract the relative variance retained. However, the data of rainfall in Peninsular Malaysia involved skewed observations in the direction of higher values with pure tendencies of values that are positive. Therefore, using Pearson correlation which was basing on PCA on rainfall set of data has the potentioal to influence the partitions of cluster as well as producing exceptionally clusters that are eneven in a space with high dimension. For current research, to resolve the unbalanced clusters challenge regarding the patterns of rainfall caused by the skewed character of the data, a robust dimension reduction method in PCA was employed. Thus, it led to the introduction of a robust measure in PCA with Tukey’s biweight correlation to downweigh observations along with the optimal breakdown point to obtain PCA’s quantity of components. Outcomes of this study displayed a highly substantial progress for the robust PCA, contrasting with the PCA-based Pearson correlation in respects to the average amount of acquired clusters and indicated 70% variance cumulative percentage at the breakdown point of 0.4.]]>
<![CDATA[New concept for cryptographic construction design based on noniterative behavior ]]>
<![CDATA[Abdallah Abouchouar]]>;
<![CDATA[Fouzia Omary]]>;
<![CDATA[Khadija Achkoun]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (541.415 KB)
|
DOI: 10.11591/ijai.v9.i2.pp229-235
<![CDATA[Nowadays, cryptography especially hash functions require to move from classical paradigms to an original concept able to handle security issues and new hardware architecture challenges as in distributed systems. In fact, most of current hash functions apply the same design pattern that was proved vulnerable against security threats; hence the impact of a potential weakness can be costly. Thus, the solution begins with a deep analysis of divers attack strategies; this way can lead to finding a new approach that enables new innovative and reliable candidates as alternative hash functions. So to achieve this goal, in this article we introduce a new construction design that consists of a non-iterative behavior by combining a parallel block processing and a sequential xor addition process, in order to provide a secure design without changing the expected goal of a hash function, at the same time avoid the use of vulnerable structures.]]>
<![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 ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
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.]]>
<![CDATA[Source localization of tone perception in alcoholic brain indexed by standardized low-resolution electromagnetic tomography ]]>
<![CDATA[Vachrintr Sirisapsombat]]>;
<![CDATA[Phakkharawat Sittiprapaporn]]>;
<![CDATA[Chaiyavat Chaiyasut]]>;
<![CDATA[Sasithorn Sirilun]]>;
<![CDATA[Roungsan Chaisricharoen]]>;
<![CDATA[Thamthiwat Nararatwanchai]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (18.121 KB)
|
DOI: 10.11591/ijai.v9.i3.pp561-568
<![CDATA[Alcohol consumption is known to associate with several diseases, injuries, and social problems. The long-term, excessive alcohol exposure can lead to liver cirrhosis and pancreatitis. After repating alcohol exposure, alcohol dependence would develop an individually behavioral, cognitive, and physiological phenomenon. Previous studies indicated that although the left hemisphere was selectively employed for processing linguistic information irrespectively of acoustic cues or subtype of phonological unit, the right hemisphere was employed for prosody-specific cues. These previous studies provided the impetus for future investigations of tone perception and temporal integration differences in tonal brain speaker who had long-term, excessive alcohol exposure such as Thai in the present study. The present study used both an auditory mismatch negativity (MMN) component of event-related potentials (ERPs) recording and the standardized Low-resolution Electromagnetic Tomography (sLORETA) techniques to measure the degree of cortical activation and to localize the brain area contributing to the scalp recorded auditory MMN component during the passive oddball paradigm. Ten healthy right-handed adults participated in this study. The findings showed that both [kha:] - mid tone perception and [khá:] - high tone perception elicited a strong MMN between 215-284 ms with reference to the standard-stimulus ERPs. Source localization was obtained in the middle temporal gyrus of the right hemisphere for both [kha:] - mid tone perception and [khá:] - high tone perception. Automatic detection of tone perception in alcoholic tonal brain is a useful index of language universal auditory memory traces.]]>
<![CDATA[A real-time drowsiness and fatigue recognition using support vector machine ]]>
<![CDATA[Nur Nabilah Abu Mangshor]]>;
<![CDATA[Iylia Ashiqin Abdul Majid]]>;
<![CDATA[Shafaf Ibrahim]]>;
<![CDATA[Nurbaity Sabri]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v9.i4.pp584-590
<![CDATA[A drowsiness and fatigue problems among the drivers are the main factor that contributes to road accidents. These problems are vital to be resolved as they could contribute to damage of road facilities, vehicles and most importantly the loss of lives. In avoiding these matters, a proper mechanism is needed to alert the driver to stay awake throughout the driving journey. Thus, this study proposed a real-time prototype for recognizing the drowsiness and fatigue face expression of the driver. The methodology of this study involves facial features detection using Viola-Jones algorithm to detect the exact position of both left and right eyes and mouth. Next, based on the detected eyes and mouth beforehand, the segmentation processes performed on both eyes and mouth using Sobel edge detection to obtain facial regions. The feature extraction phase is conducted using shape-based feature to obtain the extraction values. Support vector machine (SVM) classifier is deployed for the recognition task. A total of 100 images are used during the testing stages. This study achieved a competetive result of 90.00% of accuracy. Yet, hybridization or integration of more image processing techniques will be performed in the future to improve the current accuracy obtained.]]>
<![CDATA[Fault-type coverage based ant colony optimization algorithm for attaining smaller test suite ]]>
<![CDATA[Bharathi M]]>;
<![CDATA[Sangeetha V]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1007.721 KB)
|
DOI: 10.11591/ijai.v9.i3.pp507-519
<![CDATA[In this paper, we proposed Fault-Type Coverage Based Ant Colony Optimization (FTCBACO) technique for test suite optimization. An algorithm starts with initialization of FTCBACO factors using test cases in test suite. Then, assign separate ant to each test case called vertex. Each ant chooses best vertices to attain food source called objective of the problem by means of updating of pheromone trails and higher probability trails. This procedure is repeated up to the ant reaches food source. In FTCBACO algorithm, minimal number of test cases with less execution time chosen by an ant to cover all faults type (objective) are taken as optimal solution. We measured the performance of FTCBACO against Greedy approach and Additional Greedy Approach in terms of fault type coverage, test suite size and execution time. However, the heuristic Greedy approach and Additional Greedy approach required more execution time and maximum test suite size to provide the best resolution for test suite optimization problem. Statistical investigations are performed to finalize the performance significance of FTCBACO with other approaches that concludes FTCBACO technique enriches the reduction rate of test suite and minimizes execution time of reducing test cases efficiently.]]>
<![CDATA[Classification of RRIM clone series using artificial neural network ]]>
<![CDATA[Faridatul Ama Ismail]]>;
<![CDATA[Nina Korlina Madzhi]]>;
<![CDATA[Noor Ezan Abdullah]]>;
<![CDATA[Hadzli Hashim]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (757.78 KB)
|
DOI: 10.11591/ijai.v9.i2.pp297-303
<![CDATA[This paper presents comparative investigation on the classification of rubber latex clone series using Artificial Neural Network (ANN) based on optical sensing technique. Rubber Research Institute of Malaysia (RRIM) introduced the rubber breeding program known as RRIM clone series in order to increase the yield of latex production and the rubber wood to meet the requirement for export and import in upstream sector. Due to the large numbers of clones launched with different characteristics and properties, this bring difficulty such as lack of information regarding to the identification on cloning. Therefore, this work developed an optical based sensing system for classification of the selected RRIM 2000 and 3000 clone series based. Near Infrared Sensors was used as sensing element in order to measure the latex from the top surface and photodiode which received the reflected light from the sensor via reflectance index in term of voltage. The raw obtained data was then used as input parameter for ANN tool which supervised by scaled gradient back propagation and the performance was optimized at 25 neurons with 74.4% accuracy. By using ANN the sensitivity, specificity and accuracy for each clones are measured. RRIM 3001 shows the highest sensitivity, 94.1% while RRIM 2002 shows the highest specificity of 99.1% accuracy, 93.1%. As a result, the system could differentiate RRIM 2002 more compare to other clones.]]>
