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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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Exploring the dynamics of providing cognition using a computational model of cognitive insomnia Rateb, Roqia; M. Abualhaj, Mosleh; Alsaaidah, Adeeb; A. Alsharaiah, Mohammad; Shorman, Amaal; Jaber Thalji, Nisrean
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp92-101

Abstract

Insomnia is a common sleep-related neuropsychological disorder that can lead to a range of problems, including cognitive deficits, emotional distress, negative thoughts, and a sense of insufficient sleep. This study proposes a providing computational dynamic cognitive model (PCDCM) insight into providing cognitive mechanisms of insomnia and consequent cognitive deficits. Since the support providing is significantly dynamic and it includes substantial changes as demanding condition happen. From this perspective the underlying model covers integrating of both coping strategies, provision preferences and adaptation concepts. The model was found to produce realistic behavior that could clarify conditions for providing support to handle insomnia individuals, which was done by employing simulation experiments under various negative events, personality resources, altruistic attitude and personality attributes. Simulation results show that, a person with bonadaptation and either problem focused or emotion focused coping can provide different social support based on his personality resources, personality attributes, and knowledge level, whereas a person with maladaptation regardless the coping strategies cannot provide any type of social support. Moreover, person with close tie tends to provide instrumental, emotional, and companionship support than from weak tie. Finally, a mathematical analysis was used to examine the possible equilibria of the model. 
A robust penalty regression function-based deep convolutional neural network for accurate cardiac arrhythmia classification using electrocardiogram signals Pratima, Anniah; Kanathur, Gopalakrishna; Prasad, Sarappadi Narasimha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp629-640

Abstract

Cardiac arrhythmias are a leading cause of morbidity and mortality worldwide, necessitating accurate, and timely diagnosis. This paper presents a novel approach for the classification of cardiac arrhythmias using a penalty regression function (PRF)-based deep convolutional neural network (DCNN). The proposed model integrates advanced preprocessing techniques, including frechet with fitness rank distribution-based anas platyrhynchos optimization (FFRD-APO) for feature selection and ensemble empirical mode decomposition (EEMD) for signal decomposition. Utilizing the St. Petersburg INCART 12-lead arrhythmia database, the PRF-DCNN model achieved superior performance metrics: an area under the curve-receiver operating characteristic (AUC-ROC) of 0.97, accuracy of 0.95, precision of 0.93, recall of 0.92, specificity of 0.97, and an F1 score of 0.93. The PRF effectively mitigated overfitting, ensuring robust and reliable classification across varied patient demographics. The model demonstrated significant improvements over traditional methods, offering an efficient solution for real-time cardiac monitoring and diagnosis. This study underscores the potential of PRF-DCNN in enhancing automated arrhythmia detection and lays the groundwork for future research to optimize and validate this approach in diverse clinical settings.
Fog and rain augmentation for license plate recognition in tropical country environment Wahyu Saputra, Vriza; Suciati, Nanik; Fatichah, Chastine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3951-3961

Abstract

Automatic license plate recognition (ALPR) is a critical component in modern traffic management systems. However, ALPR systems often face challenges in accurately recognizing license plates under adverse weather conditions, such as fog and rain, prevalent in tropical regions. Deep learning ALPR models necessitate huge and diverse datasets for robustness, but data availability remains a concern since unpredictable fog and rain patterns hinder data collection. In this study, we address the issue of enhancing ALPR's robustness by introducing a novel augmentation strategy that combines traditional and weather augmentation techniques. By augmenting the dataset with weather-induced variations, we aim to improve the generalization capability of ALPR models, enabling them to handle a wider range of weather-related challenges. We also investigate the synergy between these weather augmentations and established scene text recognition (STR) methods, such as convolutional recurrent neural network (CRNN), TPS-ResNet BiLSTM-attention (TRBA), autonomous bidirectional iterative scene text recognition (ABINet), vision transformer (ViTSTR), and permutated autoregressive sequence (PARSeq), to determine their impact on recognition accuracy. Experiments using different training data sets show that training data containing a combination of traditional and weather augmentation produces the best accuracy and 1-NED performance compared to training data without augmentation and traditional augmentation only. The average increase accuracy of all STR model is 1.13% with the best increase accuracy of 3.68% using TRBA.
Data-driven decisions: Artificial intelligence-based experimental validation of ocean ecosystem services scale Figueiredo, Ronnie; Cabral, Pedro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4178-4185

Abstract

Several studies address the main topic of research, ecosystem services. It is also proven that decision-making in organizations generally involves a decision-maker, who assumes internal responsibility for the results. However, when the decision is collective, we need to think about the context of governance. How can we increase the sustainable decisions of ocean ecosystem services governance? When this decision is applied to ocean ecosystem services, in particular, we need a parameter. Therefore, the proposed scale is an initial guide for support key decision-makers decisions on the governance of ocean services ecosystems. The scale proposal with validation through classical linear regression, and supported by an artificial neural network, demonstrates the main variables that influence the decision and contribute to possible risk mitigations in terms of decisions. 
Enhancing interpretability in random forest: Leveraging inTrees for association rule extraction insights Hilali Moh’d, Fatma; Anwar Notodiputro, Khairil; Angraini, Yenni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4054-4061

Abstract

The random forest model is a powerful supervised learner, recognized for its ability to learn the pattern within data with superior predictive accuracy. However, it is a black box model because it lacks interpretability. This study addressed the interpretable challenge by employing the inTree framework. The rules were extracted from each decision tree in a random forest model, and the association rules were determined through measured matrix support and confidence to reveal the frequent variable interactions for predicting unemployment. This approach provided insight into the relationships between specific variables and unemployment outcomes. The developed method used data set from the integrated labor force survey (ILFS) 2020/2021 in Zanzibar. Zanzibar’s unemployment rate consistently increased across surveys conducted in 2006, 2014, and 2020/2021. Results have shown that the rules that most predict unemployment for individuals are female and lack of health insurance and secondary education level, female and youth age group and lack of health insurance and secondary education level with a high confidence level. This study provides practical insights for Zanzibar’s government to develop effective interventions, programs, and policies. Improving the interpretability of the random forest model enhances decision-making to address unemployment challenges.
SQL-CB-GuArd: a deep learning mechanism for structured query language injection attack detection Sirmulla, AsifIqbal; Manickam, Prabhakar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp337-349

Abstract

Structured query language (SQL) injection attacks, which take advantage of input field vulnerabilities to introduce malicious code into database queries, are a serious danger to database-driven programs and systems. Intruders can now alter, recover, or remove sensitive data because of illegal access. Strong artificial intelligence (AI) based security solutions are required to reduce SQL injection threats, as these assaults' significance highlights. This study's main goal is to create automated AI-based techniques that can identify structured query language injection attack (SQLIA) in real time eliminating the need for human intervention. Although machine learning (ML) and deep learning-based techniques have received a lot of interest in this field, MLbased techniques have problems with accuracy and false negatives. Deep learning (DL) is therefore commonly used in these text data processing and natural language processing (NLP) applications. We have introduced a hybrid DL approach for SQLIA detection in this paper. The pre-processing step performs decoding, generalization, and tokenization to improve the learning performance. The proposed approach uses combination of convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU) with attention mechanism. The combination helps to improve the pattern learning capacity. The proposed approach is validated on publically available data and experimental analysis reported that the proposed SQL-CB-GuArd achieves better accuracy of SQLIA detection.
Internet of things and blockchain integration for security and privacy Kumar, Sumita; Vidhate, Amarsinh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4037-4044

Abstract

The internet of things (IoT) can be defined as a network of intelligent objects where physical objects are equipped with electronic and network components to enable connectivity. These smart objects are embedded with sensors that enable them to monitor, sense, and gather data pertaining to their surroundings, including the environment and human activities. The applications of IoT, both existing and forthcoming, show great promise in terms of enhancing convenience, efficiency, and automation in our daily lives. However, for the widespread adoption and effective implementation of the IoT, addressing concerns related to security, authentication, privacy, and recovery from potential attacks is crucial. To achieve end-to-end security in IoT environments, it is imperative to define standard framework to achieve end to end security for the IoT applications. The blockchain is distributed ledge offers advantages such as confidentiality, authenticity, and availability. In this paper, we propose a novel framework to provide security and privacy for heterogeneous IoT architecture with integration of blockchain. The framework has provided an assessment framework to deploy, govern physical deployment. The proposed framework has defined standard architecture to integrate blockchain with layered IoT architecture with customization in blockchain with lightweight cryptography and consensus mechanism to overcome integration challenges and to achieve authenticity, security, and privacy.
An artificial intelligence approach to smart exam supervision using YOLOv5 and siamese network I. Zanoon, Nabeel; A. Alhaj, Abdullah; Alkharabsheh, Khalid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3920-3929

Abstract

Artificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analysing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyses images and live video clips without human intervention. Deep learning models can be applied to digital images to identify and classify objects accurately. Object detection algorithms are based on deep learning algorithms in artificial intelligence. Using the deep learning algorithm, object detection is achieved with high accuracy. In this paper, a combined model of the YOLOv5 model and network Siames technology is proposed, in which the YOLOv5 algorithm detects cheating tools in classrooms, such as a cell phone or a book, in such away that the algorithm detects the student as an object and cannot recognize his face. Using the Siames network, we compare the student’s face against the data base of students in order to identify the student with cheating tools.
Balancing and metaheuristic techniques for improving machine learning models in brain stroke prediction Aouragh, Abd Allah; Bahaj, Mohamed; Toufik, Fouad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp473-481

Abstract

A brain stroke, medically referred to as a stroke, represents a critical condition triggered by the disruption of blood flow to a region of the brain. Early detection of stroke is crucial to prevent fatal complications. In this study, we worked with an unbalanced dataset of 4981 entries on stroke, which we balanced using the K-means synthetic minority over-sampling technique (KMeansSMOTE) algorithm. We then employed five machine learning algorithms: decision tree, random forest, support vector machine, K-nearest neighbors, and gradient boosting. We compared the hyperparameter optimization of these algorithms using four metaheuristic techniques: gray wolf optimization, particle swarm optimization, genetic algorithm, and artificial bee colony. The models' effectiveness was evaluated using multiple metrics, such as accuracy, recall, precision, F1-score, and area under the receiver operating characteristic curve. Our findings indicate that the random forest optimized by the genetic algorithm achieved the best performance, with an accuracy of 97.39% and an F1-score of 97.35%. This study highlights the effectiveness of balancing and metaheuristics techniques in optimizing machine learning models for stroke forecasting.
Detecting fraudulent financial statement under imbalanced data using neural network Tjahyadi, Hendra; Efraim Young, Yosua
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4106-4112

Abstract

In this paper a novel approach for detecting fraudulent financial statements by employing a combination of neural networks and synthetic minority over-sampling technique (SMOTE) is introduced. This approach is designed to tackle the problem of imbalanced datasets prevalent in fraudulent cases, which if left unaddressed will hinder the model to accurately identify fraud. Three neural network models, each representing different fraud predictors as the input layer: 28 inputs raw financial data; 14 inputs financial ratios data; and 42 inputs combination both raw financial and financial ratios data are developed. Experimental validation using established research datasets is conducted to assess the performance of the proposed method. Performance metrics, namely area under the curve (AUC), precision, and sensitivity, are used for evaluation, comparing the proposed model against existing benchmark models found in literature. Results indicate that the proposed model achieves an AUC score of 70.6% and a precision score of 2.89%, in comparable to the existing models, with a sensitivity score of 83% outperforming all counterparts. The high sensitivity rate of the proposed model underscores its practical utility for auditors and regulators, as it minimizes the risk of false negatives, thereby enhancing confidence in fraud detection.

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