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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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Model for autism disorder detection using deep learning Sharma, Anshu; Tanwar, Poonam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp391-398

Abstract

Autism spectrum disorder (ASD) is a neurodegenerative illness that impacts individuals' social abilities. The majority of available approaches rely on structural and resting-state functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. To detect ASD with a limited dataset, the bulk of known technologies involve Machine Learning, pattern recognition, and other techniques, leading to high accuracy but moderate generality. To address this constraint and improve the efficacy of the automated autism diagnosis model, an ASD detection model based on deep learning (DL) is provided in this work. The classification challenge is solved using a convolutional neural network classifier. The suggested model beats state-of-the-art methodologies in terms of accuracy, according to simulation findings. The proposed approach investigates how anatomical and functional connectivity indicators can be used to determine whether or not a person is autistic. The proposed method delivers state-of-the-art results, with the classification of Autistic patients achieving 93.41% accuracy and the localization of the classified data regressed to 0.29 mean absolute error (MAE). 
Analysis of language identification algorithms for regional Indonesian languages Sujaini, Herry; Bijaksana Putra, Arif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1741-1752

Abstract

Detecting local languages in Indonesia is essential for recognizing linguistic diversity, promoting intercultural understanding, preserving endangered languages, and improving access to education and services. By identifying and documenting these languages, we can support language preservation efforts, provide tailored resources for communities, and celebrate the unique cultural heritage of different ethnic groups. Ultimately, this encourages a more accepting and open-minded society, prioritizing various languages and cultural customs. This research aims to identify the most suitable algorithm for language detection in Indonesian regional languages and gain insights into their unique characteristics through n-gram analysis. By understanding language diversity, the study contributes to preserving Indonesia's cultural and linguistic heritage and improving language detection techniques. This study compares the performance of five algorithms (Naïve Bayes, K-nearest neighbors (KNN), least-squares, Kullback Leibler divergence, and Kolmogorov Smirnov test) to determine the most accurate and efficient method for language identification. Incorporating trigram features alongside unigrams and bigrams significantly improved the model's performance, with F1 scores increasing from 0.923 to 0.959. The study found that using more features leads to better accuracy, with Naïve Bayes and KNN emerging as the top-performing algorithms for language identification.
Sectoral electricity micro-spatial load forecasting based on partitional clustering technique Senen, Adri; Jamani Jamian, Jasrul; Satya Dini, Hasna; Supriyanto, Eko; Anggaini, Dwi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3533-3544

Abstract

Load demand forecasting is crucial in energy supply planning due to economic progress and territorial expansion, where land utilization transforms dynamically. An accurate sectoral load prediction can preclude the loss of beneficial opportunities arising from excessive load demand or excessive investment at a low-growth juncture. However, the particular area in this sectoral approach is still relatively large, rendering it incapable of precisely projecting load at minor points (micro-spatial). This study has proposed a micro-spatial load prediction strategy that categorizes identified areas into smaller grids or districts. This procedure includes clustering similar sites together for improved accuracy. K-Means is one of the partitional clustering approaches, a clustering algorithm utilizing object-based centroid-based partitioning approaches. The algorithm determines a cluster's centroid or centre as the average point for the cluster. This technique is advantageous as it can process extensive data efficiently and is appropriate for circular data. This technique can divide the data into multiple partitions, ensuring that each object belongs to precisely one cluster. Subsequently, mathematical modelling is used to predict the load of each cluster, which can then be utilized to more accurately evaluate the positions and sizes of prospective substations, transmission, and distribution facilities.
Early detection of tomato leaf diseases based on deep learning techniques Najim, Mohammed Hussein; Abdulateef, Salwa Khalid; Alasadi, Abbas Hanon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp509-515

Abstract

Tomato leaf diseases are a big issue for producers, and finding a single method to combat them is tough. Deep learning techniques, notably convolutional neural networks (CNNs), show promise in recognizing early indicators of illness, which can help producers avoid costly concerns in the future. In this study, we present a CNN-based model for the early identification of tomato leaf diseases to preserve output and boost yield. We used a dataset from the plantvillage database with 11,000 photos from 10 distinct disease categories to train our model. Our CNN was trained on this dataset, and the suggested model obtained an astounding 96% accuracy rate. This shows that our method has the potential to be efficient in detecting tomato leaf diseases early on, therefore assisting producers in managing and reducing disease outbreaks and, as a result, resulting in higher crop yields.
Novel computational intelligence-based model for effective traffic management in intelligent transportation system Appaji, Impana; Pandian, Raviraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2524-2539

Abstract

The evolution of intelligent transportation system (ITS) is essentially meant for upgrading the driving experience with more safety and accessibility of various analytical information from its extensive network. However, a significant gap is observed that doesn't cater up the complete demands of ITS. It has been also noted that computational intelligence (CI) based approach is slowly gaining pace in solving the transport related problems in ITS as compared to its other counterpart existing methodologies like artificial intelligence. The proposed manuscript introduces a novel computational framework towards assisting in relaying routing and navigational services using CI-based approach. A design of novel navigational controller unit is presented for global ITS scenario towards yielding an optimal decision of routing. The CI-based approach is implemented by integrating fuzzy process with evolutionary searching, learning and probability theory in most simplified form. The study also introduces a novel concept of relaying decision as feedback from navigational controller unit to specific vehicular node discretely unlike existing traffic controller system with an agenda to offer faster and effective clearance of queued vehicular nodes from target area. The study outcome shows higher consistency in its relay with better performance from existing study model in ITS.
Comparison and evaluation of YOLO models for vehicle detection on bicycle paths Garcia-Pajuelo, Joshue; Paiva-Peredo, Ernesto Alonso
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3634-3643

Abstract

Non-permitted vehicles have taken over bicycle lanes in various Latin American cities as an alternative escape from traffic. Still, they do not foresee the risk to which they expose users of smaller vehicles, such as cyclists. Technological advancement has made researchers use deep learning (DL) to solve various problems in a city's traffic. However, no research has been found focusing on any issue of vehicles allowed or prohibited to travel on a bicycle lane. Therefore, in this article, the you only look once (YOLO) algorithm was used, taking the lightest models from the YOLOv4 to the most recent version, YOLOv8, to detect 05 classes of vehicles that transit or interfere in a bicycle lane, such as bicycles (Bi), motorcycles (Mo), electric motorcycles (ME), electric scooters (SE) and motorcycle cabs (Mt). When testing with the test images, the YOLOv8m model in 50 epochs, using a batch size of 32 and SGD optimizer, was the most optimal, obtaining F1 results with 88.00%, mAP@0:50 of 94.80% and mAP@0.50:0.95 of 76.60%, also had a training time of 1:28h using a Nvidia T4 GPU from Google Colab.
Enhancing intrusion detection system using rectified linear unit function in pigeon inspired optimization algorithm Tedyyana, Agus; Ghazali, Osman; W. Purbo, Onno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1526-1534

Abstract

The increasing rate of cybercrime in the digital world highlights the importance of having a reliable intrusion detection system (IDS) to detect unauthorized attacks and notify administrators. IDS can leverage machine learning techniques to identify patterns of attacks and provide real-time notifications. In building a successful IDS, selecting the right features is crucial as it determines the accuracy of the predictions made by the model. This paper presents a new IDS algorithm that combines the rectified linear unit (ReLU) activation function with a pigeon-inspired optimizer in feature selection. The proposed algorithm was evaluated on network security layer - knowledge discovery in databases (NSL-KDD) datasets and demonstrated improved performance in terms of training speed and accuracy compared to previous IDS models. Thus, the use of the ReLU activation function and a pigeon-inspired optimizer in feature selection can significantly enhance the effectiveness of an IDS in detecting unauthorized attacks.
The rise of artificial intelligence: a concise review Rama Padmaja, Chinimilli Venkata; Lakshmi Narayana, Sadasivuni; Latha Anga, Gouthami; Kumari Bhansali, Priyanka
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2226-2235

Abstract

Artificial intelligence (AI) has emerged as a transformative force with farreaching implications across various domains. This research review provides a concise analysis of the rise of AI, examining its evolution, applications, ethical considerations, and future prospects. The study traces the historical development of AI, highlighting key milestones and technological advancements that have propelled its growth. It explores the wide-ranging applications of AI in sectors such as healthcare, finance, transportation, manufacturing, human resource management and entertainment, showcasing its impact on efficiency, decisionmaking, and user experiences. Ethical considerations surrounding AI, including bias, privacy, and societal implications, are thoroughly discussed. The transformative potential of AI in shaping society is expolred, with insights into its effects on employment, education, governance, and societal challenges. Looking ahead, the review identifies emerging technologies and discusses challenges related to data privacy, security, and transparency. The research review concludes by emphasizing the importance of responsible and ethical development of AI, while underscoring the need for continued research and collaboration to fully harness its potential. This review serves as a valuable resource for researchers, and practitioners seeking a holistic understanding of the rise of AI and its implications.
Efficient plant leaf detection through machine learning approach based on corn leaf image classification Pujar, Premakumari; Kumar, Ashutosh; Kumar, Vineet
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1139-1148

Abstract

Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the improved k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
A novel approach to optimizing customer profiles in relation to business metrics Elveny, Marischa; Nasution, Mahyuddin K. M.; Zarlis, Muhammad; Efendi, Syahril; Syah, Rahmad B. Y.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp440-450

Abstract

Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.

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