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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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Implementation and evaluation of Heskes self organizing map counter propagation network for face recognition Olagunju, Kazeem Michael; Oke, Alice Oluwafunke; Falohun, Adeleye Samuel; Adebiyi, Marion O.
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.pp204-212

Abstract

Face recognition has attracted a lot of interest in the fields of computer vision and pattern recognition given its extensive applications in security, surveillance, and human-computer interaction. Many linear and non-linear classifiers have been introduced to bring about effectiveness in face recognition, however, the problem of occlusion, light conditions and changes in face persist. The Heskes-self-organizing map (SOM) counter propagation network (CPN) model leverages the competitive learning and self-organizing features of SOM CPN with Heskes layer to improve the effectiveness and accuracy of face recognition systems. Heskes-SOM CPN was implemented and evaluated on MATLAB R2016a using 600 images captured with the aim of digital camera. The implemented model was trained with 360 face images and tested with 240 face images using accuracy, sensitivity, specificity, and false positive rate as performance metrics at four distinct threshold values of 0.23, 0.35, 0.50, and 0.75. The major objective of the research was achieved by investigating with 50×50 and 200×200 face dimensions. Empirical results and statistical evidence established that Heskes-SOM CPN has high accuracy of approximately 97.92%, high specificity of 98.33%, high sensitivity of 99.44%, and a very low filter performance rating (FPR) of 1.67%. Therefore, Heskes-SOM CPN is presented as a novel CPN model for face recognition.
Reliable backdoor attack detection for various size of backdoor triggers Rah, Yeongrok; Cho, Youngho
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.pp650-657

Abstract

Backdoor attack techniques have evolved toward compromising the integrity of deep learning (DL) models. To defend against backdoor attacks, neural cleanse (NC) has been proposed as a promising backdoor attack detection method. NC detects the existence of a backdoor trigger by inserting perturbation into a benign image and then capturing the abnormality of inserted perturbation. However, NC has a significant limitation such that it fails to detect a backdoor trigger when its size exceeds a certain threshold that can be measured in anomaly index (AI). To overcome such limitation, in this paper, we propose a reliable backdoor attack detection method that successfully detects backdoor attacks regardless of the backdoor trigger size. Specifically, our proposed method inserts perturbation to backdoor images to induce them to be classified into different labels and measures the abnormality of perturbation. Thus, we assume that the amount of perturbation required to reclassify the label of backdoor images to the ground-truth label will be abnormally small compared to them for other labels. By implementing and conducting comparative experiments, we confirmed that our idea is valid, and our proposed method outperforms an existing backdoor detection method (NC) by 30%p on average in terms of backdoor detection accuracy (BDA).
An ensemble framework augmenting surveillance cameras for detecting intruder clusters as potential mobs Esan, Omobayo Ayokunle; Osunmakinde, Isaac O.
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.pp4557-4571

Abstract

Many developing nations around the world curtail crimes through video surveillance technology, but the crime rate is still high. This is compounded by short-staffed security operatives and a deficiency of security infrastructure to assist security operatives with knowledge-driven decision support systems in the low-resource constraint environment. In a public environment, it is challenging to detect intruder clusters accurately as potential mobs for early warning. Previous research investigated some classical techniques, but their recommendations were insufficient. This research develops a machine learning 3-tiers ensemble framework, which integrates gray level co-occurrence matrices (GLCM) principles to enhance the capabilities of surveillance cameras and security operatives to effectively discern and respond to potential mob formations. The University of California San Diego (UCSD) pedestrian datasets that are publicly available were used for the experiments. With an improved overall average precision of 0.98, recall of 0.98, and accuracy of 98.52% on the UCSD dataset, the suggested framework outperforms the widely used methods for the detection of intruder clusters. The reduction in computational time on processors showcases the framework's significant advancements as a promising solution for robust real-time threat assessment applications.
The integrated smart system to assist elderly at home Wicaksono, Handy; Santoso, Petrus; Sugiarto, Indar; Gondowardoyo, Sean; Wijaya, Andrew; Halim, Jason; Hafizah Kamarudin, Nazhatul
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.pp4988-4997

Abstract

Some elders prefer to live independently rather than in a nursing home. However, they need to be assisted because of health and safety reasons. We developed an integrated system consisting of a smart home, a mobile robot, and a wearable device, using message queuing telemetry transport (MQTT) protocol to communicate with each other. The smart home can be monitored and controlled via a mobile application. We use the telepresence robot equipped with a light detection and ranging (LIDAR) sensor to perform simultaneous localization and mapping (SLAM) and autonomous navigation. The wearable device application is used to detect older adults' heart rate, the steps taken, and the burnt calories. We also add pose detection and location estimation using a depth camera powered by an efficient deep-learning algorithm. We also developed an Android application as a dashboard that monitors and controls all system components. Our system can facilitate communication between its various components.
Application of artificial intelligence in music generation: a systematic review Gutiérrez Paitan, Frank Manuel; Acuña Meléndez, María; Ovalle, Christian
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.pp3715-3726

Abstract

Our analysis explores the benefits of artificial intelligence (AI) in music generation, showcasing progress in electronic music, automatic music generation, evolution in music, contributions to music-related disciplines, specific studies, contributions to the renewal of western music, and hardware development and educational applications. The identified methods encompass neural networks, automation and simulation, neuroscience techniques, optimization algorithms, data analysis, and Bayesian models, computational algorithms, and music processing and audio analysis. These approaches signify the complexity and versatility of AI in music creation. The interdisciplinary impact is evident, extending into sound engineering, music therapy, and cognitive neuroscience. Robust frameworks for evaluation include Bayesian models, fractal metrics, and the statistical creator-evaluator. The global reach of this research underscores AI's transformative role in contemporary music, opening avenues for future interdisciplinary exploration and algorithmic enhancements.
Improving lithium-ion battery reliability through neural network remaining useful life prediction Zraibi, Brahim; Mansouri, Mohamed
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.pp83-91

Abstract

The reliable performance of lithium-ion batteries is crucial for the safe and efficient operation of electrical systems, particularly in electric vehicles. To mitigate the risk of battery failure due to degradation, accurate forecasting of the remaining useful life (RUL) is imperative. In this study, we propose employing various recurrent neural network (RNN) methods, including RNN, gated recurrent unit (GRU), and long short-term memory (LSTM), to enhance RUL prediction accuracy for lithium-ion batteries. Our approach aims to provide reliable, accurate, and simple estimates of remaining battery life, facilitating effective management of electric vehicle power systems and minimizing the risk of failure. Performance evaluation metrics such as mean absolute error (MAE), R-squared (R²), mean absolute percentage error (MAPE), and root mean squared error (RMSE) are utilized to assess prediction accuracy. Experimental validation conducted using the NASA lithium-ion battery dataset demonstrates the superiority of LSTM in reducing prediction error and enhancing RUL prediction performance compared to alternative approaches. These findings underscore the potential of neural network methodologies in advancing battery management practices and ensuring the longevity and reliability of lithium-ion battery systems.
A terrain data collection sensor box towards a better analysis of terrains conditions Olivier Akansie, Kouame Yann; Biradar, Rajashekhar C.; Rajendra, Karthik; Devanagavi, Geetha D.
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.pp4388-4402

Abstract

Autonomous mobile robots are increasingly used across various applications, relying on multiple sensors for environmental awareness and efficient task execution. Given the unpredictability of human environments, versatility is crucial for these robots. Their performance is largely determined by how they perceive their surroundings. This paper introduces a machine learning (ML) approach focusing on land conditions to enhance a robot’s locomotion. The authors propose a method to classify terrains for data collection, involving the design of an apparatus to gather field data. This design is validated by correlating collected data with the output of a standard ML model for terrain classification. Experiments show that the data from this apparatus improves the accuracy of the ML classifier, highlighting the importance of including such data in the dataset.
Machine learning for mental health: predicting transitions from addiction to illness Alkhazraji, Ali; Alsafi, Fatima; Dbouk, Mohamed; Ibrahim, Zein Al Abidin; Sbeity, Ihab
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.pp385-396

Abstract

The increasing prevalence of infection-causing diseases due to environmental factors and lifestyle choices has strained the healthcare system, necessitating advanced techniques to save lives. Disease prediction plays a crucial role in identifying individuals at risk, enabling early treatment, and benefiting governments and health insurance providers. The collaboration between biomedicine and data science, particularly artificial intelligence and machine learning, has led to significant advancements in this field. However, researchers face challenges related to data availability and quality. Clinical and hospital data, crucial for accurate predictions, are often confidential and not freely accessible. Moreover, healthcare data is predominantly unstructured, requiring extensive cleaning, preprocessing, and labeling. This study aims to predict the likelihood of patients transitioning to mental illness by monitoring addiction conditions and constructing treatment protocols, with the goal of modifying these protocols accordingly. We focus on predicting such transformations to illuminate the underlying factors behind shifts in mental health. To achieve this objective, data from an Iraqi hospital has been collected and analyzed yielding promising results. 
Developing standard criteria for robotic process automation candidate process selection Yadav, Neelam; P. Panda, Supriya
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.pp4291-4300

Abstract

Robotic process automation (RPA) is a cutting-edge technology that provides software robots to repeat and mimic the repeatable tasks that a human user earlier performed. The use of software robots is encouraging because of their cost efficiency and easy implementation. Selecting and prioritizing a candidate process for automation is always challenging as all the business processes in an organization are not equally suitable for RPA implementation. Various studies have highlighted several criteria found in the literature for determining, prioritising, and selecting a business process for RPA. Nevertheless, there are no set standards for evaluating and analyzing a certain process or its tasks to determine whether they may be automated to use RPA. This paper aims to develop standard criteria and propose a consistent model to select and prioritize candidate process for RPA projects. To assess these criteria's applicability in the context of RPA, surveys among subject matter experts (SMEs) are used to validate them. Principal component analysis (PCA) and correlation are used to identify the top 20 criteria. Naïve Bayes algorithm is applied on the collected data for decision-making. The developed multi-criteria model exhibits strong precision and recall measures, with training and validation accuracy of 96% and 90%, respectively.
CryptoGAN: a new frontier in generative adversarial network-driven image encryption Bhat, Ranjith; Nanjundegowda, Raghu
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.pp4813-4821

Abstract

There is a growing need for an image encryption scheme, for huge amount of social media data or even the medical data to secure the privacy of the patients or the user. This study introduces a ground-breaking deep learning architecture named crypto generative adversarial networks (CryptoGAN), a novel architecture for generating cipher images. This architecture has the ability to generate both encrypted and decrypted images. The CryptoGAN system consists of an initial encryption network, a generative network that verifies the output against the desired domain, and a subsequent decryption phase. The generative adversarial networks (GAN) are utilised as the learning network to generate cipher images. This is achieved by training the neural network using images encrypted from a conventional image encryption scheme such as advanced encryption standards (AES), and learning from the resulting losses. This enhances security measures when dealing with a large dataset of photos. The assessment of the performance metrics of the encrypted image, including entropy, histogram, correlation plot, and vulnerability to assaults, demonstrates that the suggested generative network may get a higher level of security.

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