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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 4: December 2024" : 123 Documents clear
A systematic assertive wide-band routing using location and potential aware technique Mohammed Saifuddin, Karur; D. Devangavi, Geetha
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.pp3892-3899

Abstract

Delays occur when packets must be routed over several paths in a wireless sensor network with multiple origins and destinations. There are several causes, delays may occur everywhere, even in a multi-hop wireless network. Due to the broadcast nature of wireless networks, opportunistic routing was able to circumvent these delays. To avoid unnecessary delays, wide-band routing may be used to calculate the smaller path between two nodes. In this case, we address the shortcomings of the standard approach by taking into account the node's power. Path routing as well as the broadcast nature of wireless signals help mitigate the effects of shoddy wireless connections. The results show that the suggested approach outperformed the baseline in both end-to-end latency and packet delivery ratio.
Evaluation of distributed denial of service attacks detection in software defined networks S., Neethu; Aradhya, H. V. Ravish
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.pp4488-4498

Abstract

Software-defined networking (SDN) revolutionizes networking by separating control logic and data forwarding, enhancing security against threats like distributed denial of service (DDoS) attacks. These attacks flood control plane bandwidth, causing SDN network failures. Recent studies emphasize the efficacy of machine learning (ML) and statistical approaches in identifying and mitigating these security risks. However, there has been a lack of focus on employing ensembling techniques, amalgamating diverse ML models, selecting pertinent features, and utilizing oversampling techniques to balance categorical data. Our study evaluates 20 machine-learning models, emphasizing feature engineering and addressing class imbalance using synthetic minority oversampling technique (SMOTE). The results indicate that ensemble methods such as light gradient boosting machine (LGBM) classifier, random forest classifier, XGB classifier, decision tree classifier obtained near-perfect scores (almost 100%) across all metrics, suggesting potential overfitting. Conversely, models like AdaBoost classifier, k-neighbors classifier, and support vector classifier (SVC) exhibited slightly lower (99%) but realistic performance, underscoring the intricacy of accurate prediction in cybersecurity. Simpler models, including logistic regression, linear discriminant analysis, and Gaussian naive Bayes, demonstrated moderate to low accuracy, approximately around 70%. These findings stress the imperative need for a nuanced approach in the selection and fine-tuning of ML models to ensure effective DDoS detection in SDN environments. 
Implementation of fuzzy logic approach for thalassemia screening in children Redy Susanto, Erliyan; Syarif, Admi; Warsito, Warsito; Nisa Berawi, Khairun; Ayu Sangging, Putu Ristyaning; Wantoro, Agus
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.pp4062-4070

Abstract

Thalassemia is one of the most dangerous blood disorders that can lead to severe complications. It is an inherited disease, usually detected after a child is two to four years old. Identification of thalassemia is a complex task, involving many variables. Doctors generally diagnose thalassemia by using a complete blood count (CBC) and high-performance liquid chromatography (HPLC) test results. However, HPLC tests are expensive and time consuming, hence the need for other methods to identify thalassemia. There are many studies on the application of artificial intelligence for medical applications. In this study, we developed a new fuzzy-based approach to identify thalassemia based on a patient’s blood laboratory results. First, we analyzed the CBC data for blood disorder prediction. Secondly, we adopt the test results of peripheral blood smear (PBS) to identify whether the person has thalassemia. We conducted several experiments using 30 (thirty) hospital patient data and the results were compared with the results provided by experts. The experimental results show that the system can determine blood disorders with 93% accuracy and 100% precision in thalassemia prediction. This system is very effective to help doctors in diagnosing thalassemia patients.
Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system Kamelia, Lia; Zaki Hamidi, Eki Ahmad; Muhammad Fadilla, Reno
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.pp4113-4120

Abstract

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.
A novel pairwise based convolutional neural network for image preprocessing enhancement Ravi, Chaitra; Gaddadevara Matt, Siddesh
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.pp4095-4105

Abstract

Wildfires are untamable and devastating forces that impact both urban and rural regions. While predicting wildfires is challenging, efforts are made to mitigate the damage they inflict. The previous researches have limitations such as not being able to find a small region of fire in the dataset. In this research, pairwise region-based convolutional neural network (PR-CNN) is proposed for wildfire detection. The dataset used for wildfire detection is the fire luminosity airborne-based machine learning evaluation (FLAME) dataset that is pre-processed through normalization and hue, saturation, and lightness (HSV) color space to improve the image quality. Pre-processed images are taken as input to region-based convolutional neural network (R-CNN) for detection, the R-CNN has a region proposal layer that is enhanced by pairwise region and named PR-CNN. These wrapped images are fed into CNN architecture to extract and features to detect wildfire. Additionally, post processing technique like soft-non-maximum suppression (NMS) is utilized to eliminate the duplicate detection from PR-CNN for enhancing the detection accuracy. The proposed method achieves a higher accuracy of 97.44%, a precision of 97.32%, recall of 97.31%, and f1-score of 96.67%, which is comparatively superior to the existing algorithms like recurrent neural network (RNN), and R-CNN.
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.
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.
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.

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