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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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Deep neural network for lateral control of self-driving cars in urban environment El Farnane, Abdelhafid; Youssefi, My Abdelkader; Mouhsen, Ahmed; El Ihyaoui, Abdelilah
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.pp1014-1021

Abstract

The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control. 
Chatbot with ChatGPT technology for mental wellbeing and emotional management Andrade Arenas, Laberiano; Yactayo-Arias, Cesar
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.pp2635-2644

Abstract

There is a growing concern among the world's population about mental health in work, academics, and other contexts where stress, anxiety, and depression are common problems that negatively impact mental health. This study evaluates a chatbot powered by ChatGPT, offering a novel perspective on emotional intervention and mental well-being. It highlights the urgency of this approach in a context where mental health is critical, providing value by combining advanced technologies with emotional management. A multi-faceted approach was implemented to evaluate both usability and technical performance. The usability of the chatbot was evaluated by users using the System Usability Scale (SUS), while the technical performance was evaluated by experts. The active participation of 15 users provided a detailed perspective, resulting in an average usability of 83, reflecting a positive experience in interacting with the system. At the same time, five experts, through technical metrics, assigned an average technical performance of 4.28, indicating solid operational effectiveness. In conclusion, although more research is needed to customize and optimize chatbots over the long term, this approach holds promise for addressing mental health issues in a variety of settings and represents the integration of artificial intelligence to the benefit of those seeking help managing emotional disorders.
Design of smoke detection system using deep learning and sensor fusion with recursive feature elimination cross-validation Julian, James; Bagas Dewantara, Annastya; Wahyuni, Fitri
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.pp1658-1667

Abstract

The fire safety system is an important component that controls material and immaterial losses. Fire disasters are generally indicated by the appearance of excess smoke and changes in temperature, pressure, and changes in other parameters in the environment. Conventional smoke sensors are limited in reading parameter changes around their environment, making them less effective in early fire detection. This study aims to design a smoke detection system as an early fire detection system, using sensor fusion based on deep learning using the recursive feature elimination method with cross-validation (RFECV) using a random forest classifier used to select optimal parameters from public datasets as the basis for determining the sensor to be used. Based on the RFECV optimal feature, a deep learning algorithm was performed and obtained an accuracy of 0.99, a precision of 0.99, a recall of 1.00, and an F1 score of 0.99, with a latency time of 34.02 μs, which is 71.76% times faster than the original model.
An ensemble-based approach for effective distributed denial of service attack detection in software defined networking Ahmed, Mohammed Majid; Abdulkader, Hasan
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.pp2019-2026

Abstract

Software defined networking (SDN) is a network framework that aims to redefine network characteristics through the programmability of network components, faster and larger network monitoring, centralized network operation, and effective detection of fraudulent traffic and special malfunctions. However, SDN networks are vulnerable to security threats that can cause complete network failure. To address this issue, in this paper, machine learning techniques are suggested for the swift detection of attacks. Various methods for detecting distributed denial of service (DDoS) attacks are evaluated, and the study identifies the most precise method for categorizing such attacks within a SDN network. The results indicate that the proposed system achieves high accuracy in detecting DDoS attacks, with ensemble learning achieving 99% accuracy. This indicates a remarkable improvement percentage in comparison to the approaches of decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM).
A survey of detecting leaf diseases using machine learning and deep learning in various crops Thangamuthu, Thilagraj; Kareem, Abdul; Kumara, Varuna; Udesh Naik, Utkrishna; Poojary, Sanjana; R, Bharath
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.pp2498-2505

Abstract

For agricultural productivity and food security to be guaranteed, early detection and treatment of illnesses are crucial. Machine learning (ML) and deep learning (DL) approaches can be used to precisely and successfully identify plant leaf diseases. A heterogeneous dataset comprising photos of both healthy and diseased leaves such as bacterial blights, fungal infections, and viral manifestations provides the foundation for the model building and training. Accuracy, precision, recall, and F1-score are the measures used to assess the model's performance. ML techniques are helpful in the identification and extraction of pertinent information from plant leaf pictures, whereas DL techniques in general, and convolutional neural networks (CNN), in particular, are remarkable at learning complex hierarchical representations. Therefore, DL architectures like CNN are utilized in conjunction with ML approaches like support vector machines (SVM), decision trees, and random forests to extract complicated patterns and attributes from leaf pictures. This research provides an extensive analysis of the performance and application of DL and ML approaches recently applied to the early identification of leaf diseases in different crops.
A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems Arief, Muhammad; Gunawan, Made; Septiadi, Agung; Wibowo, Mukti; Pragesjvara, Vitria; Supriatna, Kusnanda; Satriyo Nugroho, Anto; Baskara Nugraha, I Gusti Bagus; Supangkat, Suhono Harso
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.pp1574-1584

Abstract

To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.
Intelligent automation computational modelling for contextual consulting services using Industry 4.0 Pandey, Vijay Kumar; Rathore, Neeraj; Bhosale, Narayan P
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.pp2557-2565

Abstract

The methodology towards operational management and service delivery associated with consulting firms, irrespective of any business domain, are less revisited in perspective from the automation-based process management. Adoption of Industry 4.0 has been attempted by various researchers from the business process management; however, there are less evidence of any computational model towards it. Apart from this, existing models are accompanied by various loopholes which makes its further challenging to analyze it on practical environment. Hence, the proposed study introduces a novel computational and analytical framework which is capable of performing the predictive modelling in order to meet the contextual service development and delivery demands in distributed environment. The novelty of this model is its inclusion of contextual data aggregation, contextual constraint analysis, predictive maintenence, and self-adjusting machine which are core attributes of Industry 4.0 automation standards. The study outcome shows that proposed system offers 20% cost reduction and 89% of minimized service delivery time in contrast to existing related work. Same has been also observed in benchmarked outcomes with state-of-art models.
Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery Joko Prasetyo, Sri Yulianto; Sulistyo, Wiwin; Christanto, Erwien; Hasiholan Simanjuntak, Bistok
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.pp1535-1546

Abstract

The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.
Low-resolution facial emotion recognition on low-cost devices Dwisnanto Putro, Muhamad; Litouw, Jane; Poekoel, Vecky Canisius
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.pp2201-2211

Abstract

The low-resolution input image is a crucial challenge for applying facial emotion recognition in real-world scenarios. The critical problem is that valuable object features are relatively lost in the extraction process due to their small size. On the other hand, this vision system is required by a machine to run smoothly on lowcost devices. Facial emotion recognition using a lightweight feature extractor is proposed in this study to effectively capture crucial facial components in a lowresolution image. To compromise the running speed, this work offers an efficient feature convolution to discriminate specific facial features. In addition, the system is embedded with an attentive module to capture important features and correlate them. Our model performance is evaluated on low-resolution public datasets achieving the accuracy of 97.34%, 81.10%, and 80.12% on Karolinska directed emotional faces (KDEF), real-world affective faces database (RFDB), and facial expression recognition 2013 plus (FER2013Plus), respectively. The practical application demands that the deep learning model can operate fast on inexpensive devices. Consequently, the model achieved a speed of 290 frames per second (FPS) on a central processing unit (CPU) device.
Choosing allowability boundaries for describing objects in subject areas Lolaev, Musulmon; Madrakhimov, Shavkat; Makharov, Kodirbek; Saidov, Doniyor
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.pp329-336

Abstract

Anomaly detection is one of the most promising problems for study and can be used as independent units and preprocessing tools before solving any fundamental data mining problems. This article proposes a method for detecting specific errors with the involvement of experts from subject areas to fill knowledge. The proposed method about outliers hypothesizes that they locate closer to logical boundaries of intervals derived from pair features, and the interval ranges vary in different domains. We construct intervals leveraging pair feature values. While forming knowledge in a specific field, a domain specialist checks the logical allowability of objects based on the range of the intervals. If the objects are logical outliers, the specialist ignores or corrects them. We offer the general algorithm for the formation of the database based on the proposed method in the form of a pseudo-code, and we provide comparison results with existing methods.

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