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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 83 Documents
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Search results for , issue "Vol 14, No 5: October 2025" : 83 Documents clear
Artificial intelligence-powered smart roads: leveraging orange3 for traffic signs recognition Arabiat, Areen; Altayeb, Muneera; Salama, Sanaa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3816-3826

Abstract

Traffic sign recognition systems are an important concern of advance driver assistance systems (ADAS) and intelligent autonomous vehicles. Recently, many studies have emerged that aim to employ artificial intelligence (AI) and machine learning (ML) to detect and classify traffic signs to improve a system that can be embedded in vehicles to increase efficiency and safety. This work's primary goal is to address traffic sign identification and recognition utilizing a 2,339-image open-source dataset from Kaggle. Our detection model for extracting and classifying traffic sign suggestions is built using Orange3 data mining tools, based on four classifiers random forest (RF), k-nearest neighbors (KNN), decision tree (DT), and adaptive boosting (AdaBoost). Signs are classified into eight categories: don't go signs, go signs, horn signs, roundabout signs, danger signs, crossing signs, speed limit sign, and unallowed signs. The results of examining and evaluating the proposed model based on the performance evaluation metrics showed that RF outperformed with an accuracy rate of 99.8%, followed by AdaBoost with a classification accuracy of 99.2%, and the classification accuracy of DT and KNN was 98.3% and 94.9%, respectively.
Exploring social media sentiment patterns for improved cyberbullying detection Yafooz, Wael M. S.; Yahya, Abdulsamad Ebrahim; Alsaeedi, Abdullah; Alluhaibi, Reyadh; Jamil, Faisal; Elsayed, Mahmoud Salaheldin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4211-4225

Abstract

Cases of online bullying and aggressive behaviors directed at social media users have surged in recent years. These behaviors have had negative impacts on victims from a wide range of demographic groups. While efforts have been made to address persistent digital harassment, the expected outcome has been limited due to the lack of effective tools to quickly identify cyberbullying behaviors and censor them accordingly on social media platforms. This study presents a scalable and systematic method to detect and analyze offensive behavior and bullying on Twitter (now known as X). Our methodology involves extracting textual, user-related, and network-related attributes to understand the traits of individuals involved in such behaviors. This approach aims to recognize distinctive characteristics that set them apart from regular users. This study proposes a novel model by employing an integrated deep-learning model, combining the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN). This model aims to classify X comments into offensive and non-offensive categories. The proposed model’s efficiacy has been evaluated through several experiments by combining three widely recognized datasets of hate speech. The proposed model achieves an accuracy rate of approximately 98.95%, showing promising results in identifying and categorizing offensive behavior in cyberbullying.
Arithmetic artificial bee colony optimization algorithm with flexible manipulator system Hashim, Mohd Ruzaini; Mazlan, Ahmad Fitri; Tokhi, Mohammad Osman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3790-3801

Abstract

The artificial bee colony (ABC) algorithm, a well-known swarm intelligence-based metaheuristic inspired by the food foraging behavior of honeybees, has been widely applied to solve complex optimization problems. Despite its effectiveness, the standard ABC algorithm suffers from drawbacks such as slow convergence rates, limited balance between exploration and exploitation, and a tendency to get stuck in local optima, thereby hindering its overall performance. This study introduces an enhanced variant of the ABC algorithm, integrating the exploration strategy of the arithmetic optimization algorithm (AOA) to overcome these limitations. The enhanced algorithm is thoroughly tested on a set of benchmark functions as well as a flexible manipulator system model. Comprehensive statistical analyses are employed to evaluate and compare the performance of the improved algorithm against the original ABC. The results demonstrate that the enhanced ABC algorithm delivers superior performance in both benchmark scenarios and the flexible manipulator application.

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