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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 120 Documents
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Search results for , issue "Vol 13, No 1: March 2024" : 120 Documents clear
Applying Bayesian networks in making intelligent applications for static and dynamic unbalance diagnosis Romahadi, Dedik; Fitri, Muhamad; Feriyanto, Dafit; Hidayat, Imam; Imran, Muhammad
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.pp174-184

Abstract

One of the problems often encountered in vibration analysis is unbalanced or imbalanced, namely the occurrence of a shift in the center of mass from the center of rotation to cause high vibrations. Unbalance itself is divided into two, namely static and dynamic unbalance. Identification of the right type of unbalance must be done because each type of unbalance requires different handling. Therefore, this study aims to design a system to identify the type of unbalance based on the required parameters. The system design determines the input and then builds an algorithm by combining vibration analysis methods and Bayesian networks (BN). Systems and applications are built using MATLAB. After the application is finished, testing is carried out using vibration measurement data obtained from a demo machine that has previously been conditioned for damage. The BN method has been successfully applied to the unbalance diagnosis system. When there is evidence of large amplitude in 1X the frequency spectrum and the value of the static phase range, the percentage of static unbalance from 26.8% increases to 75%. The system can predict all testing data quickly and precisely for the six experiments.
Early stroke disease prediction with facial features using convolutional neural network model Ahmad, Ali; Usama, Muhammad; Niaz Khan, Yasir
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.pp933-940

Abstract

Past researcher has proposed computed tomography (CT) and magnetic resonance image (MRI) scan images as the most efficient ways to diagnose stroke disease. These methods are not only hectic and take much time but are also costly. This paper proposes a new approach to diagnosing this disease and gives a time and cost-efficient solution. We have offered a two-step solution to diagnose stroke disease in a patient using only the patient’s facial image. In the first step, we gathered a dataset of several stroke patients and normal persons. Then we applied several pre-processing operations, including red, green and blue (RGB) to grayscale conversion, scaling/ resizing, and normalization on dataset images before training them. In the second step, we trained the cropped images of their face regions and trained them using a convolutional neural network (CNN). We have successfully achieved an efficiency of 98%. The accuracy, precision, recall, and f-measure of the results were measured at 98%, 97%, 99%, and 98% respectively.
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). 
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.
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.
Classifying electrocardiograph waveforms using trained deep learning neural network based on wavelet representation Jawad, Noor Yahya; Merza, Ahmed Mohammed; Sim, Hussein Tami
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.pp408-416

Abstract

Due to the rise in cardiac patients, an automated system that can identify different heart disorders has been created to lighten and distribute the duty of physicians. This research uses three different electrocardiograph (ECG) signals as indicators of a person's cardiac problems: Normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF). The continuous wavelet transform (CWT) provides the mechanism for classifying the 190 individual cases of ECG data into a 2-dimensional time-frequency representation. In this paper, the modified GoogLeNet is used for ECG data classification. Using a transfer learning approach and adjustments to parts of the output layers, ECG classification was conducted and the effectiveness of convolutional neural network (CNN) designs was tested. By comparing the results that the optimized neural network and GoogLeNet both had classification accuracy about of 80% and 100%, respectively. The GoogLeNet provide the best result in term of accuracy and training time.
Cognitive routing in software defined networks using learning models with latency and throughput constraints Tumakuru Anadanaiah, Nagaraju; Panduranga Rao, Malode Vishwanatha
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.pp756-763

Abstract

To address latency and throughput challenges in software defined networks (SDNs), the research investigates cognitive routing's revolutionary implications. In today's data-driven world, network performance optimisation is crucial. Cognitive routing is a dynamic and potentially disruptive network management technology. Cognitive routing, strengthened by reinforcement learning and adaptive decision-making, is crucial to network efficiency and responsiveness, according to our study. The results show that cognitive routing optimises performance by limiting delay and maximising throughput. SDN application cognitive routing engine (CRE) driving forces, design, and preliminary assessment are described in this article. The CRE finds almost optimal paths for a user's quality of service (QoS) need while minimising monitoring overhead. Instead of global monitoring to find optimal paths, local monitoring achieves this. In ad-hoc networks, finding a trustworthy path reduces latency and ensures network stability. The proposed system was simulated utilising many parameters. Compared to previous SDN-based systems, end-to-end latency and ping round-trip time were better.
Defending against label-flipping attacks in federated learning systems using uniform manifold approximation and projection Upreti, Deepak; Kim, Hyunil; Yang, Eunmok; Seo, Changho
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.pp459-466

Abstract

The user experience can be greatly improved by using learning models that have been trained using data from mobile devices and other internet of things (IoT) devices. Numerous efforts have been made to implement federated learning (FL) algorithms in order to facilitate the success of machine learning models. Researchers have been working on various privacy-preserving methodologies, such as deep neural networks (DNN), support vector machines (SVM), logistic regression, and gradient boosted decision trees, to support a wider range of machine learning models. The capacity for computing and storage has increased over time, emphasizing the growing significance of data mining in engineering. Artificial intelligence and machine learning have recently achieved remarkable progress. We carried out research on data poisoning attacks in the FL system and proposed defence technique using uniform manifold approximation and projection (UMAP). We compare the efficiency by using UMAP, principal component analysis (PCA), Kernel principal component analysis (KPCA) and k-mean clustering algorithm. We make clear in the paper that UMAP performs better than PCA, KPCA and k-mean, and gives excellent performance in detection and mitigating against data-poisoning attacks. 
Predictive maintenance framework for assessing health state of centrifugal pumps Mallioris, Panagiotis; Diamantis, Evangelos; Bialas, Christos; Bechtsis, Dimitrios
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.pp850-862

Abstract

Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running and standstill values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.

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