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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
A benchmark of health insurance fraud detection using machine learning techniques Cherkaoui, Ossama; Anoun, Houda; Maizate, Abderrahim
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.pp1925-1934

Abstract

Health insurance fraud is a complex problem that also has a significant financial impact. Recently, with the availability of large volumes of data and the evolution of computing power, machine learning techniques have become the preferred method for fraud detection. However, the main difficulty facing researchers in this field is the lack of real data sets and the absence of reliable fraud labels. Most published studies use aggregated provider-level or simulated data to test fraud detection algorithms, which may not deliver accurate results. The present study aims to provide a more accurate assessment of fraud detection methods by using real detailed health insurance claims data to compare six of the most common supervised classification algorithms including neural networks and the use of two categorical feature preparation methods. The study was conducted under the guidance of insurance experts, who provided the fraud label inference rules and reviewed the results. A comprehensive description of the benchmarking process and an interpretation of the results are provided in this paper. The results show that supervised classification can be used effectively to detect health insurance fraud, improving detection accuracy by a factor of 4.2 (84% recall for a positive rate of 20%). 
Aspect based sentiment analysis using fine-tuned BERT model with deep context features Rajan, Abraham; Manur, Manohar
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.pp1250-1261

Abstract

Sentiment analysis is the task of analysing, processing, inferencing and concluding the subjective texts along with sentiment. Considering the application of sentiment analysis, it is categorized into document-level, sentence-level and aspect level. In past, several researches have achieved solutions through the bidirectional encoder representations from transformers (BERT) model, however, the existing model does not understand the context of the aspect in deep, which leads to low metrics. This research work leads to the study of the aspect-based sentiment analysis presented by deep context bidirectional encoder representations from transformers (DC-BERT), main aim of the DC-BERT model is to improvise the context understating for aspects to enhance the metrics. DC-BERT model comprises fine-tuned BERT model along with a deep context features layer, which enables the model to understand the context of targeted aspects deeply. A customized feature layer is introduced to extract two distinctive features, later both features are integrated through the interaction layer. DC-BERT mode is evaluated considering the review dataset of laptops and restaurants from SemEval 2014 task 4, evaluation is carried out considering the different metrics. In comparison with the other model, DC-BERT achieves an accuracy of 84.48% and 92.86% for laptop and restaurant datasets respectively.
Control system optimisation of biodiesel-based gas turbine for ship propulsion Machmudah, Affiani; Bakar, Elmi Abu; Rajendran, Raj; Nugroho, Wibowo Harso; Solihin, Mahmud Iwan; Ghofur, Abdul
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.pp1992-2002

Abstract

Reducing a gas emission of shipping transportations become a main goal of international maritime organization to achieve a clean energy. One of best scenarios to achieve this goal is to shift a fossil fuel to a renewable energy-based fuel of a ship propulsion. This paper studies an optimization of a control system of the renewable-based small gas turbine engine for the ship propulsion. Proposed control system consists of a proportional-integral with engine performance limiters to avoid an engine damage. Proportional-integral gains are tuned by a whale optimization algorithm. A gain scheduling analysis of a step response is performed to obtain a searching area of tuning parameters and values of constant gains. In this step, the gains are modeled as function of plant variables. After the searching area is obtained, the proportional-integral gains are optimized using the whale optimization algorithm while the additional gains are set as constant values. Using this scenario, stable and optimal gains have been successfully achieved. Results show that the proposed method has better performance than that of the previous methods, i.e. gain scheduling and gain scheduling optimized by the whale optimization algorithm. The proposed method has lowest fitness value and does not have an overshoot problem.
Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis Bel, Felcia; Selvaraj, Sabeen
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.pp2011-2018

Abstract

Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method  has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers.
Classification of nutmeg ripeness using artificial intelligence Bil Qisthi, Imam; Siswono, Hartono
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.pp2441-2450

Abstract

Nutmeg seeds can produce a lot of oil if they have optimal maturity. In other words, they have little moisture content. Based on observations made at one of the refineries in Sukabumi, farmers do not pay attention to the maturity level of nutmeg seeds after drying which can cause a decrease in the quality of nutmeg seeds and the quality of the oil produced. This study aims to make it easier for nutmeg farmers to classify the maturity of nutmeg seeds. This study used the convolutional neural network (CNN) method to help with classification problems and several image processing methods. This program will be run through an Android application. When the application containing this CNN model is run, the camera system will turn on, and the program will classify in real-time nutmeg objects into 1 of 3 class labels namely LowQuality, MidQuality, or HighQuality class labels classifying. The results will be displayed on the application screen, the results are displayed in the form of class names and scores. The results of CNN model training accuracy are 97.92%.
An boosting business intelligent to customer lifetime value with robust M-estimation Elveny, Marischa; Y. Syah, Rahmad B.; M. Nasution, Mahyuddin K.
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.pp1632-1639

Abstract

When a business concentrates too much on acquiring new clients rather than retaining old ones, mistakes are sometimes made. Each customer has a different value. Customer lifetime value (CLV) is a metric used to assess longterm customer value. Customer value is a key concern in any commercial endeavor. When there are variations in customer behavior, CLV forecasts the value of total customer income when the data distribution is not normal, and outliers are present. Robust M-estimation, a maximum likelihood type estimator, is used in this study to enhance CLV data. Through the minimization of the regression parameter from the residual value, robust Mestimation eliminates data outliers in customer metric data. With an accuracy of 94.15%, R-square is used to gauge model performance. This research shows that CLV optimization can be used as a marketing and sales strategy by companies.
Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application Mangasuli, Sushant; Kaluti, Mahesh
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.pp1348-1357

Abstract

Climate change poses several environmental threats like floods to urban environment; thus, effective and reliable communication of emergency information is needed during massive breakdown of network infrastructure. This paper presents a mobile adhoc network (MANETs) based effective information such as calls, image, and videos communication system that is compatible with current 3GPP and 5G communication network. Here in maintaining connectivity the information is communicated between different MANET nodes in a multi-hop manner. However, designing radio propagation is challenging considering higher local emergency request congestion at different terrain with varying speed of users. The current radio propagation model is designed without considering the effect of line-of-sight between communicating device and are not adaptive to different environment considering urban disaster management environment. This paper develops an adaptive radio propagation (ARP) model namely expressway, city and semiurban. Then, in reducing congestion and improving network performance efficiency the work introduced an adaptive medium access control (AMAC) protocol. The MAC incorporates a dynamic network controller (DNC) to optimize the contention window size in dynamic manner according to current traffic demands. The AMAC protocol achieves much improved throughput with lesser packet loss in comparison with existing MAC (EMAC) model considering different radio propagation model introduced in this work.
Chelonia mydas detection and image extraction from field recordings Amir Zakry, Khalif; Syahiran Soria, Mohamad; Hipiny, Irwandi; Ujir, Hamimah; Hassan, Ruhana; Hardi, Richki
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.pp2354-2363

Abstract

Wildlife videography is an essential data collection method for conducting. The video recording process of an animal like the Chelonia mydas sea turtles in its habitat requires setting up special camera or by performing complex camera movement whilst the camera operator maneuvers over its complicated habitat. The result is hours of footage that contains only some good data that can be used for further animal research but still requires human input in filtering it out This presents a problem that artificial intelligence models can assist, especially to automate extracting any good data. This paper proposes usage of machine learning models to crop images of endangered Chelonia mydas turtles to help prune through hundreds and thousands of frames from several video footages. By human supervision, we extracted and curated a dataset of 1,426 good data from our video dataset and used it to perform transfer learning on a you only look once (YOLO)v7 pre-trained model. Our paper shows that the retrained YOLOv7 model when run through our remaining video dataset with various confidence scores can crop images in the field video recordings of Chelonia mydas turtles with up to 99.89% of output correctly cropped thus automating the data extraction process.
Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification AbuSalim, Samah; Zakaria, Nordin; Maqsood, Aarish; Saboor, Abdul; Kwang Hooi, Yew; Mokhtar, Norehan; Jadid Abdulkadir, Said
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.pp2081-2092

Abstract

Effective and intelligent methods to classify medical images, especially in dentistry, can assist in building automated intra-oral healthcare systems. Accurate detection and classification of teeth is the first step in this direction. However, the same class of teeth exhibits significant variations in surface appearance. Moreover, the complex geometrical structure poses challenges in learning discriminative features among the tooth classes. Due to these complex features, tooth classification is one of the challenging research domains in deep learning. To address the aforementioned issues, the presented study proposes discriminative local feature extraction at different granular levels using you only look once (YOLO) models. However, this necessitates a granular intra-oral image dataset. To facilitate this requirement, a dataset at three granular levels (two, four, and seven teeth classes) is developed. YOLOv5, YOLOv6, and YOLOv7 models were trained using 2,790 images. The results indicate superior performance of YOLOv6 for two-class classification achieving a mean average precision (mAP) value of 94%. However, as the granularity level is increased, the performance of YOLO models decreases. For, four and seven-class classification problems, the highest mAP value of 87% and 79% was achieved by YOLOv5 respectively. The results indicate that different levels of granularity play an important role in tooth detection and classification.
Smart traffic forecasting: leveraging adaptive machine learning and big data analytics for traffic flow prediction Moumen, Idriss; Abouchabaka, Jaafar; Rafalia, Najat
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.pp2323-2332

Abstract

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

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