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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.
Arjuna Subject : -
Articles 123 Documents
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Search results for , issue "Vol 13, No 2: June 2024" : 123 Documents clear
Fault-tolerant control of a permanent magnet synchronous motor based on hybrid control strategies using a multi-variable filter Bahiddine, Miloud; Belhamra, Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2111-2121

Abstract

This paper describes direct torque control (DTC) of a permanent magnet synchronous machine (PMSM) powered by a two-level voltage inverter whose switching of switches is based on the space vector modulation (SVM). To enhance the robustness of control in the presence of faults, we have enhanced DTC with SVM by incorporating a multivariable filter to address fault current effects. In this modification, traditional proportionalintegral controllers are substituted with sliding mode controllers. This approach results in a revised structure known as DTC-SVM with sliding mode control, presenting a new fault-tolerant control diagram. A comparative study between these different control strategies is carried out. The simulation results show clearly the interest of a multi-variable filter association with the DTC-SVM-sliding mode control2 (SMC2) in degraded mode. The outcomes achieved provide substantial evidence supporting the resilience and efficacy of this approach, especially when confronted with the influence of the faults current.
A survey on plant leaf disease identification and classification by various machine-learning technique Pujar, Premakumari; Kumar, Ashutosh; Kumar, Vineet
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1187-1194

Abstract

An overview of methods for identifying plants diseases is given in this article. Each sample is categorized by being divided into various groups. The approach of classification involves identifying healthy and diseased leaves based on morphological traits including texture, color, shape, and pattern, among others. Sorting and categorizing plants can be challenging, especially when doing so across a large area, due to the closeness of their visual qualities. There are several methods based on computer vision and image processing. Selecting the right categorization method can be difficult because the outcomes rely on the data you supply. There are several applications for the categorization of plant leaf diseases in fields like agriculture and biological research. This article gives a summary of several approaches currently in use for identifying and categorizing leaf diseases, as well as their benefits and drawbacks. These approaches include preprocessing methods, feature extraction and selection methods, datasets employed, classifiers, and performance metrics
Factor analysis influencing Mobile JKN user experience using sentiment analysis Al Qahar, Muhammad Yazid; Ruldeviyani, Yova; Mukharomah, Ulfah Nur; Fidyawan, Miftahul Agtamas; Putra, Ramadhoni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1782-1793

Abstract

Social security administration for health or Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan), as a public legal entity, has a critical role in the health of the Indonesian population. BPJS Kesehatan introduced the Mobile national health insurance or jaminan kesehatan nasional (JKN) application to enhance its services, enabling Indonesians to access it directly. Nevertheless, the rating of the Mobile JKN application on the Google Play Store has shown a gradual decline over time. Therefore, this study was conducted to analyze the factors influencing the user experience of the Mobile JKN application, utilizing the review data obtained from the Google Play Store. Sentiment analysis using the Naïve Bayes (NB) classification model and support vector machine (SVM) combined with synthetic minority oversampling technique (SMOTE) and slang word replacement. The results obtained an accuracy value of 93.33%, precision of 93.76%, recall of 93.33%, and F1-score of 93.43%. A further analysis was conducted using online service quality factors to obtain the main factors influencing the experience of Mobile JKN application users. The evaluation findings revealed that factors of security, ease of use, and timeliness are three fundamental aspects that should be given immediate attention by BPJS Kesehatan while improving the Mobile JKN application in the future.
A multilingual semantic search chatbot framework R, Vinay; B U, Thejas; Vibhav Sharma, H A; Ghuli, Poonam; G, Shobha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2333-2341

Abstract

Chatbots are conversational agents which interact with users and simulate a human interaction. Companies use chatbots on their customer-facing sites to enhance user experience by answering questions about their products and directing users to relevant pages on the site. Existing chatbots which are used for this purpose give responses based on pre-defined frequently asked questions (FAQs) only. This paper proposes a framework for a chatbot which combines two approaches-retrieval from a knowledge base consisting of question answer pairs, combined with a natural language search mechanism which can scan through the paragraphs of text information. A feedback-based knowledge base update is implemented which provides continuous improvement in user experience. The framework achieves a result of 81.73 percent answer matching on stanford question answering dataset (SQuAD) 1.1 and 69.21 percent answer matching on SQuAD 2.0. The framework also performs well on languages such as Spanish (67.32 percent answer match), Russian (61.43 percent answer match), and Arabic (51.63 percent answer match). By means of zero shot learning.
Crowd navigation for dynamic hazard avoidance in evacuation using emotional reciprocal velocity obstacles Fachri, Moch; Prasetyo, Didit; Damastuti, Fardani Annisa; Ramadhani, Nugrahardi; Susiki Nugroho, Supeno Mardi; Hariadi, Mochamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1371-1379

Abstract

Crowd evacuation can be a challenging task, especially in emergency situations involving dynamically moving hazards. Effective obstacle avoidance is crucial for successful crowd evacuation, particularly in scenarios involving dynamic hazards such as natural or man-made disasters. In this paper, we propose a novel application of the emotional reciprocal velocity obstacles (ERVO) method for obstacle avoidance in dynamic hazard scenarios. ERVO is an established method that incorporates agent emotions and obstacle avoidance to produce more efficient and effective crowd navigation. Our approach improves on previous research by using ERVO to model the perceptive danger posed by dynamic hazards in real-time, which is crucial for rapid response in emergency situations. We conducted experiments to evaluate our approach and compared our results with other velocity obstacle methods. Our findings demonstrate that our approach is able to improve agent coordination, reduce congestion, and produce superior avoidance behavior. Our study shows that incorporating emotional reciprocity into obstacle avoidance can enhance crowd behavior in dynamic hazard scenarios.
Evaluation of sequential feature selection in improving the K-nearest neighbor classifier for diabetes prediction Govindarajan, Rajkumar; Balaji, Vidhyashree; Arumugam, Jayanthi; Admassu Assegie, Tsehay; Mothukuri, Radha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1567-1573

Abstract

The K-nearest neighbor (KNN) classifier employs distance metrics to measure the distance between the test instance and the samples used in training. With smaller samples, the KNN classifier achieves higher accuracy with low computational time. However, computing the distance between the test instance and all training samples to determine the class of the test instance requires higher computational time for a high-dimensional dataset. This research employs sequential feature selection (SFS) to select the optimal feature for diabetes prediction while reducing the computational time complexity of the KNN classifier. The KNN classifier showed effectiveness with an accuracy rate of 84.41% with nine features. The performance of the KNN improves by 2.6% when trained on the optimal features selected with the SFS. The result revealed glucose level, blood pressure (BP), skin thickness (ST), diabetes pedigree function (DPF), age, and body mass index (BMI) as the most representative features in diabetes prediction. The KNN classifier gives higher accuracy with these features. However, insulin and the number of times a woman is pregnant do not show a significant effect on the KNN classifier.
Evaluation of genetic algorithm in network-on-chip based architecture Radha, Doraisamy; Moharir, Minal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1479-1488

Abstract

An increase in the number of cores gives a significant bounce in performance than an improvement in any of the factors or hardware. Many core systems use network-on-chip (NoC) for efficient communications among the cores in the system. However, the problem with NoC-based communication is that it significantly consumes a large amount of power and energy because the number of routers increases with the increase in the number of cores in the system. Power consumed by such components leads to degradation of the performance. The placement of cores in the topology is non-deterministic polynomial-time hardness (NP-Hard) problem. The optimal placement of cores in NoC is essential as it minimizes latency and communication costs. Thus, the NP-Hard problem of placing cores is solved using genetic algorithm (GA) based quadtree topology. The proposed work shows the analysis of GA-based quadtree topology, which outperforms other topologies in most aspects. The performance evaluation of GA-based quadtree topology is based on latency, throughput, power, area, bisection bandwidth, and diameter. Comparing these parameters with other topologies shows the prominence of the quadtree topology. The evaluation is performed in the Booksim simulator, and the experimental results revealed that the proposed GA-based quad tree-based topology is efficient for NoC-based communications.
Balanced clustering for student admission school zoning by parameter tuning of constrained k-means Zainuddin, Zahir; Nur Risal, Andi Alviadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2301-2313

Abstract

The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2,248 prospective students and 22 public school locations. The results of testing the k-means algorithm in grouping showed the formation of non-circular patterns in the cluster membership with different numbers of centroid cluster members. In contrast, testing the constrained k-means algorithm showed balanced outcomes in cluster membership with a membership value of 103 for each school as the cluster center. The research findings state that the developed constrained k-means algorithm solves the problem of unbalanced data clustering and overlapping issues in the process of new student admissions. In other words, the constrained k-means algorithm can be a reference for the government in making decisions on new student admissions.
A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease Nastiti, Faulinda Ely; Musa, Shahrulniza; Yafi, Eiad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2036-2048

Abstract

In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating LSTM initial weights with weaknesses, using the hybrid optimization algorithm helps overcome these problems and improve model performance. The results of this study suggest that the LSTM-PSO-GWO model can be utilized as an effective and reliable predictive tool to gauge the COVID-19 virus’s spread in Indonesia. 
Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory Syakti, Agsanshina Raka; Rhamadhan, Syahri; Laziola, Ghora; Pahrizal, Pahrizal; Apdillah, Dony; Ritha, Nola
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2003-2010

Abstract

The sea brings many benefits for society, especially for a maritime country such as Indonesia. The potential in various sectors is limited only by the willingness of a party to invest in it. One such investment is in learning the knowledge and information that can be gathered from the sea, and even predicting its behavior with enough data. Using a residual LSTM algorithm, we will predict the tidal level in eastern Bintan island, a tropical island on the tip of Malay peninsula. The dataset is acquired from two sensor points in eastern Bintan coast from July 2018 to June 2019 for a span of one year, giving a total of 7,961 data points. The residual LSTM model consists of a residual wrapper with two consecutive LSTM layers and one dense layer. The model is also compared with variations of LSTM and RNN models. The result of the residual LSTM model has an MAE value of 0.1495 cm and an RMSE value of 0.3353 cm, compared to the baseline model’s 1.1148 cm and 1.4107 cm respectively. The model also has an RMSE value improvement of 76.23% compared to the base model.

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