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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 1,808 Documents
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Machine learning for potential anti-cancer discovery from black sea cucumbers Fahrury Romdendine, Muhammad; Fatriani, Rizka; Ananta Kusuma, Wisnu; Annisa, Annisa; Nurilmala, Mala
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3157-3163

Abstract

Despite being an abundant marine organism in Indonesia, black sea cucumbers (Holothuria atra) is still underutilised due to its slightly bitter taste. This study aims to identify potential anti-cancer compounds from black sea cucumbers using machine learning (ML) to perform drug discovery. ML models were used to predict interactions between compounds from the organism with cancer-related proteins. Following prediction, all compounds were computationally validated through molecular docking. The validated compounds were then screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) Lab 2.0 to assess their druglike properties. The results showed that ML predicted seven out of 86 compounds were interacted with cancer-related proteins. Computational validation from the results showed that four out of seven compounds demonstrated stable interaction with proteins where only one compound meet the criteria of drug-like compound. The framework of ML and computational validation highlighted in this study shows a great promise in the future of drug discovery specifically for marine organisms. Since computational method only works in prediction realms, wet lab validation and clinical trials are imperative before the drug candidate can be produced as actual anti-cancer drug.
Attendance management system using face recognition Jadhav, Ashish; Kamble, Dhwaniket; Rathod, Santosh B.; Kumar, Sumita; Kadam, Pratima; Dalwai, Mohammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp673-679

Abstract

Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.
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.
Measurement by applying internet financial reporting on the level of information presentation in the competitive FinTech peer-to-peer lending industry Al-Khowarizmi, Al-Khowarizmi; Efendi, Syahril; Nasution, Mahyuddin Khairuddin Matyuso; Mawengkang, Herman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp66-73

Abstract

Technological advances in the financial sector can certainly support the business decision-making process. Moreover, digital financial technology such as FinTech is a competitive industry that has both peer-to-peer (P2P) and merchant pillars. The industry must update its business activities through its information media. One of them is internet-based financial reporting or better known as internet financial reporting (IFR). IFR itself is a delivery of financial information that is carried out in real time and can be easily seen by the wider community by using the website as a medium. This study aims to determine whether the application of IFR to FinTech P2P Lending companies in Indonesia has been widely implemented or not. Later the variables used in this study are content, appearance, and timing with a total of 20 indicator variable items to be tested. The results of this paper show that 30 P2P lending FinTech Industries in Indonesia have been able to implement IFR with an average score of 80%. IFR scores obtained by each industry have almost the same value ranging from 65% to 95% with the highest total score of 95% and the lowest score of 65%.
Deep neural networks and conventional machine learning classifiers to analyze thoracic survival data Ika Agustyaningrum, Cucu; Ramdhani, Yudi; Purnama Alamsyah, Doni; B. Hariyanto, Oda I.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3686-3694

Abstract

Lung cancer is a prevalent global health concern and most prevalent malignancy in Indonesian hospitals. Following thoracic surgery, patients were categorized into two classes: individuals who experienced mortality within a year and those who achieved survival. Despite being about socks, the dataset for the deceased category consisted of 70 data samples, while the dataset for the final group comprised 400 samples. Data calculation involves the utilization of both deep neural networks and standard machine learning algorithms. The study use the Python programming language to evaluate the algorithms, and it measures their performance using metrics such as accuracy, F1-Score, precision, recall, receiver operating characteristic (ROC), and area under curve (AUC). The test results indicate that the deep neural network method achieves an accuracy of 95,56%, an F1 score of 79,24%, a precision of 91,96%, a recall of 85,52%, and an AUC of 85,52%. This study suggests that utilizing deep neural network data mining techniques, specifically with a cross-validation fold of 10, variations of six hidden layer encoder-decoder, relu, sigmoid activation function, optimizer Adam, and learning rate of 0,01, dropout rate of 0,2. Employing the Synthetic Minority Over-sampling Technique data preprocessing method, can effectively analyze thoracic patient survival data sets.
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.
Statistical performance assessment of supervised machine learning algorithms for intrusion detection system Afolabi, Hassan A.; Aburas, Abdurazzag A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp266-277

Abstract

Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network-based anomaly internet of things (N-BaIoT) and internet of things intrusion detection dataset (IoTID20). Friedman and Dunn's tests are used to statistically examine the significant differences between the classifier groups. The goal of this study is to encourage security researchers to develop an intrusion detection system (IDS) using ensemble learning and to propose an appropriate method for selecting diverse base classifiers for a stacking-type ensemble. The performance results indicate that adaptive boosting, and gradient boosting (GB), gradient boosting machines (GBM), light gradient boosting machines (LGBM), extreme gradient boosting (XGB) and deep neural network (DNN) classifiers exhibit better trade-off between the performance parameters and classification time making them ideal choices for developing anomaly-based IDSs.

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