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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 81 Documents
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Search results for , issue "Vol 14, No 1: February 2025" : 81 Documents clear
A novel model to detect and categorize objects from images by using a hybrid machine learning model Sethi, Nilambar; Rama Raju, Vetukuri Venkata Siva; Lokavarapu, Venkata Srinivas; Devareddi, Ravi Babu; Reddy, Shiva Shankar; Nrusimhadri, Silpa
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.pp667-679

Abstract

As humans, we can easily recognize and distinguish different features of objects in images due to our brain’s ability to unconsciously learn from a set of images. The objectives of this effort are to develop a model that is capable of identifying and categorizing objects that are present within images. We imported the dataset from Keras and loaded it using data loaders to achieve this. We then utilized various deep learning algorithms, such as visual geometry group (VGG)-16 and a simple net-random forest hybrid model, to classify the objects. After classification, the accuracy obtained by VGG16 and the hybrid model was 84.7% and 89.6%, respectively. Therefore, the proposed model successfully detects objects in images using a simple net as a feature extractor and a random forest for object classification, achieving better accuracy than VGG16.
Dynamic spatio-temporal pattern discovery: a novel grid and density-based clustering algorithm Meshram, Swati Pramod; Wagh, Kishor P
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.pp397-407

Abstract

Clustering is a robust machine- learning technique for exploration of patterns based on similarity of elements over multidimensional data. Spatio-temporal clustering aims to identify target objects to mine spatial and temporal dimensions for patterns, regularity, and trends. It has been applied in humancentric applications, such as recommendation systems, urban development and planning, clustering of criminal activities, traffic planning, and epidemiology to identify the extent of disease spread. Although the existing research work in the field of clustering relies widely on partition and densitybased methods, no major work has been carried out to handle the spatiotemporal dimension and understand the dynamics of temporal variation and connectivity between clusters. To address this, our paper proposes an algorithm to mine clustering patterns in spatiotemporal dataset using an adaptive, dynamic hybrid technique based on grid and density clustering. We adopt spatio-temporal partitioning of the virtual grid for distribution of data and reducing distance computation and increasing efficiency. Grouping the higher density regions along with neighborhood cluster density attraction rate to merge the clusters. This method has been experimentally evaluated over the Indian earthquake dataset and found to be effective with clustering silhouette index up to 0.93.
Convolutional neural network modelling for autistic individualized education chatbot Hamzah, Raseeda; Jamil, Nursuriati; Ahmad, Nor Diana; Syed Zainal Ariffin, Syed Mohd Zahid
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.pp109-118

Abstract

The traditional education system for autistic kids needs integration with computer technology that embraces artificial intelligence to help school instructors and management. An application that enables the teacher to retrieve information from a trusted source is essential since the information is only sometimes available on time. Thus, developing a chatbot application that utilizes natural language processing can enhance the management of autistic schools and will help individualized education for autistic students. This research uses a deep learning model that utilizes a convolutional neural network to develop a chatbot as a teaching assist tool for teachers. The results show that the chatbot has achieved ˜0.03% loss when trained with different epoch numbers. In terms of usability, the chatbot achieves mean system usability scores of 80.48 ± 13.03. This may open opportunities for more effective individualized education for students with special needs and increase the potential to improve inclusive education for disabled students. It is useful to include future actions that enable the simplification of the use of this chatbot tool in a wide range of contexts. To close the education gap for children with disabilities, chatbots could help people with communication disabilities and could also significantly enhance the rate of communication.
Serial parallel dataflow-pipelined processing architecture based accelerator for 2D transform-quantization in video coder and decoder Shivarudraiah, Sumalatha; Rajeswari, Rajeswari
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.pp798-809

Abstract

The video coder and decoder (CODEC) standards from MPEG-4 to the recent versatile video codec (VVC), adopted lossy compression methodologies, which involves transformation, quantization and entropy coding. The growing usage of video data in all means of communication demands more bandwidth and storage requirements. While compression with redundancy removal by transform coefficient coding, the focal point is the crucial sequential data flow and data processing structures. Handling the block wise data near to the processing unit prior and after computation will reduce the data waiting time of the processing unit, hence accelerating the targeted functionality. The proposed serial parallel data-flow pipelined processing architecture (SPDPA) accelerates the speed of processing unit by on chip data availability and parallel data accessing options and also with the pipeline operations of transformation, data transpose and quantization. The post implementation results of the architecture targeted to 16 nm and 28 nm field programmable gate array (FPGA) shows that there is a trade-off between power and frequency of operations for various block sizes. The design targeted to 16 nm works for higher frequencies with an average power consumption 0.64 w as compared to 28 nm FPGA which consumes less average power of 0.15 w.
Fastest Moroccan license plate recognition using a lightweight modified YOLOv5 model Fadili, Abdelhak; El Aroussi, Mohammed; Fakhri, Youssef
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.pp527-537

Abstract

Morocco is witnessing an alarming surge in road accidents. Automatic license plate recognition (ALPR) technology is vital in enhancing road safety. It en- ables applications like traffic management, law enforcement, and toll collection by automatically identifying vehicles on the roads. This paper integrated the ShuffleNet V2 architecture into the end-to-end YOLOv5 object detection sys- tem. The goal was to develop a model capable of accurately detecting Moroc- can license plates with an 87% accuracy rate. The proposed model was able to achieve high processing speeds of 60 frames per second (FPS) while maintain- ing a compact size of 1.3 megabytes and a limited computational requirement of 0.44 million floating-point operations. Compared to other models used in similar contexts, this model demonstrates superior performance and high com- patibility with embedded systems, making it a promising solution for addressing road safety challenges in Morocco.
Financial text embeddings for the Russian language: a global vectors-based approach Malyshenko, Kostyantyn A.; Anashkin, Dmitriy
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.pp692-701

Abstract

The article presents a software implementation of the linguistic embedding method for the Russian language, based on the global vectors for word representation (GloVe) model. The GloVe method allows to obtain word vectors that reflect their semantic and syntactic properties. The resulting vector model can be used in various natural language processing (NLP) tasks, such as machine translation and text clustering. The article describes the architecture of software that implements a method similar to the GloVe algorithm for Russian-language financial texts. The mechanisms used to train the model as well as to compute word vectors are described. Testing with typical classification methods demonstrated that the developed program generates accurate vector representations of Russian-language texts, proving effective in various NLP tasks. This work is one of the first studies devoted to the software implementation of the GloVe method for the Russian language using learning algorithms based on sparse matrices. The results of this study can be used in various NLP tasks, such as machine translation and text clustering.
Utilization of convolutional neural network in image interpretation techniques for detecting kidney disease Sulaksono, Nanang; Adi, Kusworo; Isnanto, Rizal
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.pp602-613

Abstract

This research is conducted with deep learning for kidney stone disease detection including cysts, stones, normal, and tumors using axial computerized tomography (CT) scan images. The author uses augmentation, generative adversarial networks (GANs), original, and synthetic minority over-sampling technique (SMOTE) to classify kidney disease (cyst, stone, normal, and tumor). This study uses the public dataset nazmul0087 and primary data/data from the hospital, using convolutional neural network (CNN) models, namely augmentation, GANs, original, and SMOTE by training and testing. The results of the accuracy value of the training model (dataset nazmul0087) in the detection of kidney cysts, stones, tumors, and normal. The results of augmentation value are 99.93%, GANs 100%, original 100%, and SMOTE 99.93%. In the results of the training model, a very high accuracy value is obtained, with perfect results. The testing model's accuracy value in detecting kidney cysts, stones, tumors, and normal kidney tissue in the original dataset and hospital data. The results of augmentation value are 11.48%, GANs 17.96%, original 21.76%, and SMOTE 20.41%. In the results of the training model, the highest accuracy value is obtained in the original model. For the testing model to automatically diagnose kidney illness and obtain a high accuracy value, which can enhance patient outcomes and save health care costs, we advise using it in conjunction with the original model.
Two-step convolutional neural network classification of plant disease Lumbantoruan, Rosni; Rajagukguk, Nico; Lubis, Anju Ucok; Claudia, Marwani; Simanjuntak, Humasak
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.pp584-591

Abstract

Indonesia is primarily an agricultural country, with farming being the primary source of income for most of its people. Unfortunately, crop production is vulnerable to plant diseases, which are usually caused by plant pests, resulting in a reduction in both the quantity and quality of the expected harvest. In addition to the large number of classes to predict, detecting and accurately classifying each disease on different plants can be difficult. We believe that limiting the number of classes to identify may improve classification accuracy. Thus, in this research, we propose a new approach, two-step convolutional neural network (CNN), which reduces the number of classes with a two-step classification approach. To begin, we identify the number of classes that can be reduced by categorizing them into different characteristics, namely, plant type classification and plant condition classification. Second, we deal with unbalanced datasets, which can result in poor performance, if overlooked. Finally, we compare the proposed two-step CNN to baseline CNN in terms of efficiency and effectiveness. Extensive experiments show that the two-step CNN outperforms the baselines, CNN and jellyfish-residual network (JF-ResNet), increasing accuracy by 4% and 2% to 99%, respectively. In addition, we also provide a simulation evaluation to ensure that this approach is applicable.
Hyperparameter optimization of convolutional neural network using particle swarm optimization for emotion recognition Rini, Dian Palupi; Sari, Tri Kurnia; Sari, Winda Kurnia; Yusliani, Novi
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.pp547-560

Abstract

Emotion identification has been widely researched based on facial expressions, voice, and body movements. Several studies on emotion recognition have also been performed using electroencephalography (EEG) signals and the results also show that the technique has a high level of accuracy. EEG signals that detected by standart method using exclusive representations of time and frequency domains presented unefficient results. Some researchers using the convolutional neural network (CNN) method performed EEG signal for emotional recognition and obtained the best results in almost all benchmarks. Although CNN has shown fairly high accuracy, there is still a lot of room for improvement. CNN is sensitive to its hyperparameter value because it has considerable effect on the behavior and efficiency of the CNN architecture. So that the use of optimization algorithms is expected to provide an alternative selection of appropriate hyper parameter values on CNN. Particle swarm optimization (PSO) algorithm is a metaheuristic-based optimization algorithm with many advantages. This PSO algorithm was chosen to optimize the hyperparameter values on CNN. Based on the evaluation results in each model, hybrid CNN-PSO showed better results and achieved the best value in 80:20 split data which is 99.30% accuracy.
Arabic text diacritization using transformers: a comparative study Zubeiri, Iman; Souri, Adnan; El Mohajir, Badr Eddine
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.pp702-711

Abstract

The Arabic language presents challenges for natural language processing (NLP) tasks. One such challenge is diacritization, which involves adding diacritical marks to Arabic text to enhance readability and disambiguation. Diacritics play a crucial role in determining the correct pronunciation, meaning, and grammatical structure of words and sentences. However, Arabic texts are often written without diacritics, making NLP tasks more complex. This study investigates the efficacy of advanced machine learning models in automatic Arabic text diacritization, with a concentrated focus on the Arabic bidirectional encoder representations from transformers (AraBERT) and bidirectional long short-term memory (Bi-LSTM) models. AraBERT, a bidirectional encoder representation from transformers (BERT) derivative, leverages the transformer architecture to exploit contextual subtleties and discern linguistic patterns within a substantial corpus. Our comprehensive evaluation benchmarks the performance of these models, revealing that AraBERT significantly outperforms the Bi-LSTM with a diacritic error rate (DER) of only 0.81% and an accuracy rate of 98.15%, against the Bi-LSTM's DER of 1.02% and accuracy of 93.88%. The study also explores various optimization strategies to amplify model performance, setting a precedent for future research to enhance Arabic diacritization and contribute to the advancement of Arabic NLP.

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