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[Age prediction from COVID-19 blood test for ensuring robust artificial intelligence ]]>
<![CDATA[Nurul Qomariyah, Nunung]]>;
<![CDATA[Kazako, Dimitar]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp3072-3082
<![CDATA[With the advancement of artificial intelligence (AI) nowadays, the world is experiencing conveniences in automating some complex and tedious tasks, such as analysing large data and predicting the future by mimicking human expertise. AI has also shown promise for mitigating future crisis, such as pandemic. Since the beginning of the COVID-19, several AI models have been published by the researchers to help the healthcare to fight in this situation. However, before deploying the model, one needs to ensure that the model is robust and safe to learn from the real environment, especially in medical domain, where the uncertainty and incomplete information are not unusual. In the effort of providing robust AI, we proposed to use patient age as one of the feasible feature for ensuring vigorous AI models from electronic health record. We conducted several experiment with 28 blood test items and radiologist report from 1,000 COVID-19 patients. Our result shows that with the predicted age as an additional feature in mortality classification task, the model is significantly improved when compared to adding the actual age. We also reported our findings regarding the predicted age in the dataset.]]>
<![CDATA[Inverse kinematic solution and singularity avoidance using a deep deterministic policy gradient approach ]]>
<![CDATA[Surriani, Atikah]]>;
<![CDATA[Wahyunggoro, Oyas]]>;
<![CDATA[Imam Cahyadi, Adha]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp2999-3009
<![CDATA[The robotic arm emerges as a subject of paramount significance within the industrial landscape, particularly in addressing the complexities of its kinematics. A significant research challenge lies in resolving the inverse kinematics of multiple degree of freedom (M-DOF) robotic arms. The inverse kinematics of M-DOF robotic arms pose a challenging problem to resolve, thus it involves consideration of singularities which affect the arm robot movement. This study aims a novel approach utilizing deep reinforcement learning (DRL) to tackle the inverse kinematic problem of the 6-DOF PUMA manipulator as a representative case within the M-DOF manipulator. The research employs Jacobian matrix for the kinematics system that can solve the singularity, and deep deterministic policy gradient (DDPG) as the kinematics solver. This chosen technique offers enhancing speed and ensuring stability. The results of inverse kinematic solution using DDPG were experimentally validated on a 6-DOF PUMA arm robot. The DDPG successfully solves inverse kinematic solution and avoids the singularity with 1,000 episodes and yielding a commendable total reward of 1,018.]]>
<![CDATA[TMS320F28379D microcontroller for speed control of permanent magnet direct current motor ]]>
<![CDATA[Chalardsakul, Tanawat]]>;
<![CDATA[Piliyasilpa, Chotnarin]]>;
<![CDATA[Sukontanakarn, Viroch]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp2816-2828
<![CDATA[This paper aims to study the behavior of the proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies the controlling voltage based on motor speed errors. Finally, the result shows the fuzzy-based tuning PI-D controller approach has a minimum overshoot, and minimum transient and steady state parameters compared to the PID controller to control the speed of the motor. The PID controllers have poorer performance due to the non-linear features of the PMDC motor.]]>
<![CDATA[Herbal plant leaves classification for traditional medicine using convolutional neural network ]]>
<![CDATA[Fauzi, Alfharizky]]>;
<![CDATA[Soerowirdjo, Busono]]>;
<![CDATA[Haryatmi, Emy]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp3322-3329
<![CDATA[The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.]]>
<![CDATA[Framework for content server placement using integrated learning in content delivery network ]]>
<![CDATA[Dharmapal, Priyanka]]>;
<![CDATA[Channakrishnaraju, Channakrishnaraju]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp3028-3038
<![CDATA[Content placement is a significant concern in content delivery networks (CDN), irrespective of various evolving studies. Existing methodologies showcase various significant unaddressed issues concerning content placement approaches' complexities. Therefore, the proposed study presents a novel computational framework towards dynamic content placement strategy using a novel integrated machine learning approach. Simplified mathematical modelling is used to formulate and solve the content placement problem. At the same time, reinforcement learning and the sequential attentional neural network have been utilized to optimize the decision-making towards placement of content servers. Designed and assessed over a Python environment, the proposed scheme is witnessed to exhibit 35% reduced bandwidth utilization, 20% reduced delay, 23% reduced computational resource utilization, and 28% reduced algorithm processing time in contrast to existing predictive content placement schemes.]]>
<![CDATA[Fuzzified input data tuning for agriculture commodities price prediction ]]>
<![CDATA[Hegde, Girish]]>;
<![CDATA[Hulipalled, Vishwanath R.]]>;
<![CDATA[Simha, Jay B.]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp2725-2735
<![CDATA[The quality of the input data will typically affect the prediction accuracy. Preprocessing of data is commonly referred to as input data tuning. Tuning the input data is critical for projecting commodity prices. Anomalies or outliers are unavoidable in historical price data. To increase prediction and forecasting accuracy, it is necessary to find and correct outliers before training the prediction model. To correct the anomaly and increase prediction accuracy, the Fuzzified Input Data Tuning and Prediction algorithm proposed in this study. The identified outliers are corrected using the relevant fuzzy set value in this method. With outlier corrected data, we used Long Short-Term Memory and Seasonal Autoregressive Integrated Moving Average to anticipate tomato prices in Karnataka state. The result of the proposed algorithm is compared with the Sliding Window anomalies correction model, and without disposing of the outliers. The suggested algorithm, with 37.89%, performed better than Sliding Window with 40.08% and 43.11% Mean Absolute Percentage Error, respectively, and without outlier correction. The sensitivity analysis shows that the performance of the model is unaffected by the forecasting horizon. Finally, comparitive analysis peformed with previous research work, and the proposed model performed better.]]>
<![CDATA[Recommendation mobile antivirus for Android smartphones based on malware detection ]]>
<![CDATA[Saputra, Hendra]]>;
<![CDATA[Zahra, Amalia]]>;
<![CDATA[Faldi, Faldi]]>;
<![CDATA[Fadzlul Rahman, Ferry]]>;
<![CDATA[Harits, Sayekti]]>;
<![CDATA[Joko Pranoto, Wawan]]>;
<![CDATA[Rahman, Fathur]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp3559-3566
<![CDATA[The proliferation of smartphone malware attacks due to a lack of vigilance in app selection raises serious concerns. Built-in smartphone security features often must be improved to protect devices from these threats. Although numerous articles recommend top-tier antivirus solutions, there need to be more reliable data sources that raise suspicions about undisclosed promotional motives. This research endeavors to establish a ranking of antivirus efficacy to provide optimal recommendations for Android smartphone users. The research methodology entails a meticulous comparison of malware detection and labeling outcomes between various antivirus programs within Virustotal and the labeling system employed by the Euphony application. The comparative results are categorized into three groups: antivirus solutions proficient in identifying specific malware types, those detecting malware presence without categorization, and antivirus software failing to detect malware effectively. The experimental findings present the five leading antivirus solutions, ranked from the highest to lowest scores, as Ikarus, Fortinet, ESET-NOD32, Avast-Mobile, and SymantecMobileInsight. Based on the comprehensive assessment conducted in this study, these solutions are recommended as the top antivirus choices. These recommendations are poised to significantly aid users in selecting the most suitable antivirus protection for their Android smartphones.]]>
<![CDATA[Insights of learning approach towards determination of potentially objectional communication in social networking ]]>
<![CDATA[Nanjundappa, Praneetha Garagadakuppe]]>;
<![CDATA[Naganna, Kamalakshi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp2506-2513
<![CDATA[Over the last decade, sentiment analysis has evolved significantly towards extracting the contextual knowledge associated with the communication exchanged in social networks. Irrespective of various approaches to natural language processing and constantly evolving machine learning, sentiment analysis has inherent shortcomings, which further act as an obstacle to determining hateful and offensive speech exchanged in social networks. Therefore, this paper offers a compact yet granular insight into the effectiveness of existing sentiment analysis approaches used distinctly for determining hateful and offensive speech with particular emphasis on machine learning-based methodologies. The paper further contributes towards research trend analysis followed by distinct highlights of the research gap. The paper offers a learning outcome that significantly benefits future researchers investigating the same field.]]>
<![CDATA[BahanbaKu: intuitive mobile application for buying recipe ingredients ]]>
<![CDATA[Brata, Komang Candra]]>;
<![CDATA[Ramadhan, Rigel Vibi]]>;
<![CDATA[Nugraha, Irvan Rizki]]>;
<![CDATA[Zhafrant, Rifqi Hilmy]]>;
<![CDATA[Putra, Ananda Ilyasa]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp2566-2573
<![CDATA[Many solutions were proposed to help people get their food or recipe ingredients easily without having to go to the market in person. Especially for people who have limited time and energy to go out and buy groceries. However, the existing solutions still lack the intuitiveness for users to get the ingredient information of a food recipe and still can’t facilitate the customers to have an all-in-one solution to get the needed ingredients. This study aims to combine the machine learning modalities and image recognition approach to provide a mobile app that can help people find food recipes intuitively and provide a digital ecosystem that can accommodate collaboration between various business actors including the MSMEs (Micro, Small, and Medium Enterprises). Instead of users buying items separately, the unique value of the proposed app (BahanbaKu) is that each of the ingredients needed by the user will be delivered in a single package. The preliminary evaluation result reveals that the accuracy of the proposed app is promising to assist people while getting recipe information for a particular food. The proposed app is also considered intuitive to the users with 81.7 SUS score.]]>
<![CDATA[Improved unmanned aerial vehicle control for efficient obstacle detection and data protection ]]>
<![CDATA[Moldamurat, Khuralay]]>;
<![CDATA[Atanov, Sabyrzhan]]>;
<![CDATA[Akhmetov, Kairat]]>;
<![CDATA[Bakyt, Makhabbat]]>;
<![CDATA[Belgibekov, Niyaz]]>;
<![CDATA[Zhumabayeva, Assel]]>;
<![CDATA[Shabayev, Yuriy]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i3.pp3576-3587
<![CDATA[The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.]]>
