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[Heart rate events classification via explainable fuzzy logic systems ]]>
<![CDATA[Anis Jannah Dahalan]]>;
<![CDATA[Tajul Rosli Razak]]>;
<![CDATA[Mohammad Hafiz Ismail]]>;
<![CDATA[Shukor Sanim Mohd Fauzi]]>;
<![CDATA[Ray Adderley JM Gining]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp1036-1047
<![CDATA[As people, we have no way of knowing whether our heart rate is considered normal or not. The strength and quality of our pulses will deteriorate as we get older. As a result, this may indicate a heart attack or another illness that requires immediate attention. The main goal of this paper is to define the heart rate stage using fuzzy logic systems (FLSs). In practice, however, designing or developing fuzzy logic systems is extremely difficult. To achieve this aim, we suggested a solution that involves: i) classifying the medical expert's criterion for signs of heart rate; ii) developing an explainable fuzzy logic system for heart rate measurement; and iii) evaluating the proposed system with human experts. In addition, the aim of this research was to provide an explainable fuzzy system that people could use to self-monitor heart rate levels and determine their health status. As a result, it is hoped that this research would provide insight into how to improve the development of fuzzy logic systems, especially in the field of medical applications.]]>
<![CDATA[Measuring scientific collaboration in co-authorship networks ]]>
<![CDATA[Basim Mahmood]]>;
<![CDATA[Nagham A. Sultan]]>;
<![CDATA[Karam H. Thanoon]]>;
<![CDATA[Dheyaa S. Kadhim]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp1103-1114
<![CDATA[Scientific research is currently considered one of the key factors in the development of our life. It plays a significant role in managing our business, study, and work more conveniently. One of the important aspects when it comes to scientific research is the level of collaboration among researchers/disciplines. The collaboration between two different disciplines contributes to obtaining more reliable solutions for our everyday issues. Therefore, it is needed to understand the collaboration patterns among researchers and come up with convenient strategies for strengthening this kind of collaboration. In this work, we aim at investigating the patterns of scientific collaboration among researchers across disciplines. To this end, we generate a co-authorship network for several disciplines. The generated network reveals many interesting facts regarding the collaboration patterns among researchers who work in the same/different disciplines. We involve several measurements in this study that evaluate different aspects, which is of interest to the research communities since most of the studies in the literature measure specific aspects. Moreover, we propose a novel metric for measuring scientific collaboration in a research community and use it to benchmark the collaboration among disciplines. Finally, we use the obtained results/facts in providing recommendations for scientific communities.]]>
<![CDATA[Enhancing digital marketing performance through usage intention of AI-powered websites ]]>
<![CDATA[Dawud Adaviruku Suleiman]]>;
<![CDATA[Tahir Mumtaz Awan]]>;
<![CDATA[Maria Javed]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp810-817
<![CDATA[Digital and wireless technology are a crucial part of today’s modern life. Artificial intelligence (AI) uses different technologies and systems for speech recognition, visual perception and decision making to mimic human functions. This study explores the impact of AI on website interactivity and the ease of use for enhancing digital marketing performance. The methodology used is qualitative with structured interviews, using three artificial intelligence-powered websites (Amazon, Alibaba, and Uber) as reference. The participants' structured interview responses were grouped into different thematic heading for coding and were subsequently analyzed by NVivo. The result found that artificial intelligence empowered websites were interactive, participants don’t feel safe and secure, easy to use, and tend to boost digital marketing performances. This study implies that more digital marketing companies should consider integrating artificial intelligence capabilities in their business operations. More security features should be embedded to help customers calm the fears of web insecurities.]]>
<![CDATA[A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization ]]>
<![CDATA[Olief Ilmandira Ratu Farisi]]>;
<![CDATA[Budi Setiyono]]>;
<![CDATA[R. Imbang Danandjojo]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp910-918
<![CDATA[This study proposed a hybrid approach of firefly algorithm (FA) and ant colony optimization (ACO) for solving multi-depot multiple traveling salesman problem, a TSP with more than one salesman and departure city. The FA is fast converging but easily trapped into the local optimum. The ACO has a great ability to search for the solution but it converges slowly. To get a better result and convergence time, we integrate FA to find the local solutions and ACO to find a global solution. The local solutions of the FA are normalized then initialized to the quantity of pheromones for running the ACO. Furthermore, we experimented with the best parameters in order to optimize the solution. In justification, we used the sea transportation route in Indonesia as a case study. The experimental results showed that the hybrid approach of FA and ACO has superior performance with an average computational time of 26.90% and converges 32.75% faster than ACO.]]>
<![CDATA[A performance evaluation of convolutional neural network architecture for classification of rice leaf disease ]]>
<![CDATA[Afis Julianto]]>;
<![CDATA[Andi Sunyoto]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp1069-1078
<![CDATA[Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.]]>
<![CDATA[Pineapple maturity classifier using image processing and fuzzy logic ]]>
<![CDATA[Edwin R. Arboleda]]>;
<![CDATA[Christian Louis T. de Jesus]]>;
<![CDATA[Leahlyne Mae S. Tia]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp830-838
<![CDATA[This paper describes the development of a prototype using an image processing system for extracting features and fuzzy logic for classifying the maturity of pineapple fruits depending on the colors of its scales. The standards that the system used are from Philippine National Standards for fresh fruits-pineapple for the 'queen' variant. The prototype automatically classified the maturity of queen pineapple variant grown in Munting Ilog, Silang, Cavite, Philippines. Data gathered are from the images loaded into the system using a camera unit under a controlled environment. The images loaded consist of the three faces of the pineapple sample, each with 120-degree coverage to capture the whole 360-degree view of the scale. The images then are sent to the system of the prototype where the features of the images are segmented based on the RGB color reduction. By using the fuzzy logic classifier, the obtained experimental results showed 100% accuracy for both the unripe and overripe maturity and 90% accuracy for the under-ripe and ripe maturity classification. The results obtained show that the developed image processing algorithm and the fuzzy-logic-based classifier could be used as an accurate and effective tool in classifying the maturity of pineapples.]]>
<![CDATA[Support vector machine based fault section identification and fault classification scheme in six phase transmission line ]]>
<![CDATA[A Naresh kumar]]>;
<![CDATA[M Suresh Kumar]]>;
<![CDATA[M Ramesha]]>;
<![CDATA[Bharathi Gururaj]]>;
<![CDATA[A Srikanth]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp1019-1024
<![CDATA[The higher complexity of a six phase transmission system (SPTS) construction and the large number of possible faults makes the protection task challenging. Moreover, the reverse & forward path faults in SPTS cannot be detected by traditional relay as it becomes under-reach. In this paper, a support vector machine (SVM) method including Haar wavelets for SPTS fault section identification and fault classification is focused. The positive-sequence component phase angle and currents at middle two buses are used to formulate a suggested method. Feasibility of suggested SVM is tested with a 138 kV, 300 km, 60 Hz, SPTS in MATLAB based Simulink platform. Several major parameters including far end and near end location conditions are taken to investigate the reach setting and accuracy of proposed SVM. This relaying method can detect the existence of fault in reverse & forward path in 1 ms time.]]>
<![CDATA[Delay aware downlink resource allocation scheme for future generation tactical wireless networks ]]>
<![CDATA[Ravi Shankar H.]]>;
<![CDATA[Kiran Kumari Patil]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp1025-1035
<![CDATA[For a very long time protecting physical border integrity is considered to be a challenging thing. Government organizations must provide trade operations for economic growth and at the same time must prevent malicious activity. A different resource such as drones, sensors, and radars are used for monitoring border areas which must be communicated to the remote border security force. Efficient wireless communication is required for communicating information. However, these devices cannot connect to a centralized network directly; thus, are connected in an ad-hoc fashion to connect centralized server. Different tactical network applications require different quality of service (QoS); hus efficient resource scheduling plays a very important role. Existing resource scheduling adopting deep learning and reinforcement techniques fails to meet the quality of experience (QoE) of the user and doesn’t assure access fairness among contending users. Further, require network information in prior and induce high training time. For overcoming research issues, this paper presents a delay-aware downlink resource scheduling (DADRA) technique for future generation networks. The optimization problem of reducing buffer overflow and improving scheduling QoS performance is solved using a genetic algorithm with an improved crossover function. Experiment outcome shows DADRA achieves much better throughput, slot utilization, and packet failure performance when compared with standard resource allocation technique.]]>
<![CDATA[A systematic literature review of machine learning methods in predicting court decisions ]]>
<![CDATA[Nur Aqilah Khadijah Rosili]]>;
<![CDATA[Noor Hidayah Zakaria]]>;
<![CDATA[Rohayanti Hassan]]>;
<![CDATA[Shahreen Kasim]]>;
<![CDATA[Farid Zamani Che Rose]]>;
<![CDATA[Tole Sutikno]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp1091-1102
<![CDATA[Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.]]>
<![CDATA[A general framework for selecting appropriate criteria of student as research assistant using fuzzy delphi method ]]>
<![CDATA[Sulaiman Abd Anter]]>;
<![CDATA[Bahbibi Rahmatullah]]>;
<![CDATA[Shihab Hamad Khaleefah]]>;
<![CDATA[Khairul Fikri Tamrin]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v10.i4.pp839-846
<![CDATA[This research aims to build a general framework for choosing the most appropriate set of criteria for recruiting student as a research assistant in a university research project. University researchers could benefit from such a framework because it could optimize the costs of research while also enhancing students research skills. In the same time, it is also essential that the quality of research ought to measure up to the grants provided by the university. Nevertheless, it is a challenging problem for many research supervisors in the selection of qualified research assistants. In this paper, we attempted to resolve this problem by building a general framework for selecting the appropriate criteria in the evaluation of student performance. We explored earlier studies on the proposed evaluation criteria of the research assistant and identified 47 most impactful criteria criteria. We obtained experts in engineering and information technology fields from two universities to answer questionnaires to identify their commonly used criteria for grant research assistant (GRA). Then, all the identified criteria were evaluated using the fuzzy delphi method (FDM) for finding the best fitting criteria which resulted in 16 most impactful criteria.]]>
