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31,456 Article Results

The role of disruptive technologies in the metaverse worlds: state of the art survey

10.11591/ijece.v15i2.pp2211-2223
Jamal N. Al-Karaki , Amjad Gawanmeh , Ahmed Awad , Natnael Zerai Teklesenbet
The metaverse has emerged as an immersive and interactive virtual world that has the potential to revolutionize various industries. The use of disruptive technologies, such as blockchain, artificial intelligence (AI), digital twin, internet of things (IoT), cloud, big data, and cybersecurity, has and will play a significant role in enhancing the capabilities of the metaverse. This paper provides a state-of-the-art survey on the role of disruptive technologies in the metaverse. The paper presents a taxonomy of the use of disruptive technologies in the metaverse and a comprehensive literature review on the application areas of the metaverse in education, healthcare, tourism, gaming, and smart cities. The paper compares the adoption of technologies in the metaverse and identifies current and future research directions. The paper contributes to understanding disruptive technologies’ potential in the metaverse. It provides insights for researchers, practitioners, and policymakers to explore the opportunities and challenges of the metaverse.
Volume: 15
Issue: 2
Page: 2211-2223
Publish at: 2025-04-01

Enhancing spatiotemporal weather forecasting accuracy with 3D convolutional kernel through the sequence to sequence model

10.11591/ijece.v15i2.pp2022-2030
Renaldy Fredyan , Karli Eka Setiawan , Kelvin Asclepius Minor
Accurate weather forecasting is important when dealing with various sectors, such as retail, agriculture, and aviation, especially during extreme weather events like heat waves, droughts, and storms to prevent disaster impact. Traditional methods rely on complex, physics-based models to predict the Earth's stochastic systems. However, some technological advancements and the availability of extensive satellite data from beyond Earth have enhanced meteorological predictions and sent them to Earth's antennae. Deep learning models using this historical data show promise in improving forecast accuracy to enhance how models learn the data pattern. This study introduces a novel architecture, convolutional sequence to sequence (ConvSeq2Seq) network, which employs 3D convolutional neural networks (CNN) to address the challenges of spatiotemporal forecasting. Unlike recurrent neural network (RNN)--based models, which are time-consuming due to sequential processing, 3D CNNs capture spatial context more efficiently. ConvSeq2Seq overcomes the limitations of traditional CNN models by ensuring causal constraints and generating flexible length output sequences. Our experimental results demonstrate that ConvSeq2Seq outperforms traditional and modern RNN-based architectures in both prediction accuracy and time efficiency, leveraging historical meteorological data to provide a robust solution for weather forecasting applications. The proposed architecture outperforms the previous method, giving new insight when dealing with spatiotemporal with high density.
Volume: 15
Issue: 2
Page: 2022-2030
Publish at: 2025-04-01

A novel one-dimensional chaotic map with improved sine map dynamics

10.11591/ijece.v15i2.pp2128-2137
Mohamed Htiti , Ismail Akharraz , Abdelaziz Ahaitouf
These days, keeping information safe from people who should not have access to it is very important. Chaos maps are a critical component of encryption and security systems. The classical one-dimensional maps, such as logistic, sine, and tent, have many weaknesses. For example, these classical maps may exhibit chaotic behavior within the narrow range of the rate variable between 0 and 1and the small interval's rate variable. In recent years, several researchers have tried to overcome these problems. In this paper, we propose a new one-dimensional chaotic map that improves the sine map. We introduce an additional parameter and modify the mathematical structure to enhance the chaotic behavior and expand the interval's rate variable. We evaluate the effectiveness of our map using specific tests, including fixed points and stability analysis, Lyapunov exponent analysis, diagram bifurcation, sensitivity to initial conditions, the cobweb diagram, sample entropy and the 0-1 test.
Volume: 15
Issue: 2
Page: 2128-2137
Publish at: 2025-04-01

Optimizing queue efficiency: Artificial intelligence-driven tandem queues with reneging

10.11591/ijai.v14.i2.pp975-983
Keerthika Radhakrishnan , Subramani Palani Niranjan , Balakrishnan Komala Durga , Suresha Ramareddy
This paper delves into the theoretical integration of queueing theory and artificial intelligence (AI), examining the benefits and implications of their convergence. Queueing systems serve as fundamental models for various real-world applications, from telecommunications networks to healthcare facilities. This research presents a transformative framework for elevating the efficiency and performance of queueing systems by infusing AI-driven tandem queue analysis. The implications of this approach transcend industries, promising streamlined operations, reduced waiting times, and resource optimization. This work invites further exploration and application, offering a path to more effective and responsive queueing systems globally. Over the years, researchers and practitioners have explored numerous techniques to enhance the efficiency and performance of queueing systems. In recent times, integrating AI into the realm of queueing analysis has opened up new avenues for optimization and innovation. This paper studies a two-server tandem queueing model with reneging customers using AI techniques. Assuming that the arrival rate follows the Poisson process and the service rate follows an exponential distribution, using the birth-death process, probability generating function and AI module, we derive steady-state difference equation, expected number of people in customers, and mean waiting time.
Volume: 14
Issue: 2
Page: 975-983
Publish at: 2025-04-01

Vehicle side control of a wireless power transfer charger using optimized artificial neural network

10.11591/ijece.v15i2.pp1487-1498
Marouane El Ancary , Abdellah Lassioui , Hassan El Fadil , Anwar Hasni , Yassine El Asri , Zakariae El Idrissi
This paper investigates a new approach to control a wireless power transfer (WPT) charger for electric vehicles (EVs) employing an optimized artificial neural network (ANN). Enhancing the efficiency and robustness of such systems is crucial, and integrating artificial intelligence (AI)-based solutions has introduced innovative approaches in this field. The proposed method enables precise regulation of battery charging voltage even under challenging conditions, such as coil misalignment or shared grounding assemblies for multiple EVs. To assess the stability and robustness of the proposed controller, its performance was evaluated under scenarios of coil misalignment and shared grounding assemblies for EVs with varying battery voltages. The controller effectively eliminated overshoot and significantly reduced residual output voltage ripple by 4.33% compared to a conventional proportional-integral (PI) controller, demonstrating the superior performance and reliability of the ANN-based control approach.
Volume: 15
Issue: 2
Page: 1487-1498
Publish at: 2025-04-01

Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms

10.11591/ijece.v15i2.pp2427-2435
Gurumoorthi Gurulakshmanan , Raveendra N. Amarnath , Sivaprasad Lebaka , Munnangi Koti Reddy , Nagarajan Mohankumar , Surulivelu Muthumarilakshmi , Chelliah Srinivasan
The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.
Volume: 15
Issue: 2
Page: 2427-2435
Publish at: 2025-04-01

Nonlinear regression analysis to predict mandibular landmarks on panoramic radiographs

10.11591/ijece.v15i2.pp2098-2108
Nur Nafiiyah , Ayu Ismi Hanifah , Edy Susanto , Eha Renwi Astuti , Chastine Fatichah , Ramadhan Hardani Putra , Agus Subhan Akbar
An automatic system for determining mandibular landmark points on panoramic radiography can reduce errors due to differences in expert professionalism and save time. Previous research has shown that the linear regression method is ineffective at predicting condyle and gonion landmark points in panoramic radiography. So, this research proposes an analysis of nonlinear regression methods (support vector machine (SVM) kernel=‘polynomial’, polynomial regression, ensemble regression) for predicting condyle and gonion landmark points. There are four predicted landmark points, namely the right condyle, left condyle, right gonion, and left gonion. The nonlinear regression methods used are SVM, polynomial regression, and ensemble regression. The Dental and Oral Hospital, within the Faculty of Dentistry at Universitas Airlangga, provides the research data. The research encompasses 119 patients between the ages of 19 and 70, dividing 103 into training and 16 into testing. The research results show that the SVM method is only good at predicting the right condyle point with a mean radial error (MRE) of 4,724 pixels. Meanwhile, to predict the left condyle, right gonion, and left gonion points, it is better to use the polynomial regression method and ensemble regression with an order of success detection rate (SDR) of 37.5%, 18.75%, and 12.5%, respectively.
Volume: 15
Issue: 2
Page: 2098-2108
Publish at: 2025-04-01

A convex hull based geofencing system to eradicate COVID

10.11591/ijece.v15i2.pp1817-1825
Parul Arora , Suman Deswal
The World Health Organization (WHO) has identified coronavirus disease (COVID-19), as a global pandemic due to its quick global spread to more than 183 countries. Many countries have used movement control orders (MCO) and high alert levels to halt the spread. The primary goal of this research is to provide a geofencing architecture that is specially tailored to the MCO's standard requirement for monitoring an individual's whereabouts during a lockdown. Whenever an individual tests Corona positive, Geofencing uses technology to notify an anticipated network of people who may be affected and to enable traceability for potential patients. Computational techniques such as Delaunay triangulation (inpolygon) and triangle weight characterization (inside polygon) are applied to analyze the geographical boundary in which the patient is isolated. Convex hull, on the other hand, is a better technique than computational algorithms. It is considered the best mathematical technique because it takes the least amount of time (0.014985 sec) to detect the patient within the geofence layer and has the lowest standard deviation when compared with the other computational techniques.
Volume: 15
Issue: 2
Page: 1817-1825
Publish at: 2025-04-01

High speed space division multiplexing based integrated fiber transmission system and its impact on atmospheric conditions

10.11591/ijece.v15i2.pp1745-1753
Kakarla Phaneendra Kumar , T. Sreenivasulu Reddy
With several advantages over traditional wireless and fiber optic cables, free space optical communication is becoming more and more popular because of its high speed and bandwidth applications. In particular, the high transmission capacity of free space optics (FSO) communication systems makes them preferred, which enables them to handle the growing amount of internet traffic. Because FSO has no regulatory limitations, operates over large distances with high levels of security, and transmits data faster than radio frequency communication technologies are widely utilized. However, given different atmospheric conditions, the availability and capacity of FSO optical bands represent a major concern. For next-generation information transmission networks to satisfy end users high-capacity demands, the transmission technology known as space-division multiplexing (SDM) has become essential. Hence in this work, High speed space division multiplexing based integrated fiber/FSO transmission system and its impact on atmospheric conditions is presented. The SDM transmission system developed in this study is based on an integrated multimode fiber (MMF)/ FSO link with linear polarized (LP) spatial modes. This approach takes into consideration extreme climate conditions like snow and turbulence while evaluating performance. Utilizing bit error rate (BER) and received optical power (ROP) are the performance of FSO is evaluated in different weather conditions.
Volume: 15
Issue: 2
Page: 1745-1753
Publish at: 2025-04-01

Comparison design of dynamic voltage restorers, distribution static compensators and unified power quality conditioner series shunts on voltage sag, and voltage swell

10.11591/ijece.v15i2.pp1396-1410
Yulianta Siregar , Syahrun Mubarok , Nur Nabila Mohamed
One issue with the power system is electrical power quality, which is brought on by short circuit disruptions and growing nonlinear loads. Power systems frequently have short circuits, resulting in voltage sags that can harm delicate loads. Voltage sage and swell issues can be resolved using unified power quality conditioner series shunts (UPQC-S), distribution static compensators (DSTATCOM), and dynamic voltage restorers (DVR). Custom power devices are very useful in overcoming problems with electrical networks. In this research, due to 3-phase short circuit faults, voltage sag and swell simulations were conducted using a load equal to 70% of the total load and a fault location point of 75% of the feeder length, from the results of research conducted with the case study PT. PLN (Persero) UP3 Sibolga Feeder SB 02 shows that DVR performs better than DSTATCOM and UPQC-S in handling voltage sag and voltage swell due to 3-phase short circuit disturbances. The DVR succeeded in providing the largest voltage sag recovery in phase C, increasing the voltage from 0.2481 pu to 0.9776 pu. The DVR is also effective in overcoming voltage swell on phase A, reducing it from 1.724 pu to 0.9969 pu.
Volume: 15
Issue: 2
Page: 1396-1410
Publish at: 2025-04-01

Novel features extraction: pigment epithelial detachment detection using machine learning algorithms

10.11591/ijece.v15i2.pp1572-1583
Sheeba Thankappan Mercy , Albert Antony Raj Saminathan , Anand Murugadhas , Anshy Princella Anand Sheeba
The majority of the retinal diseases have visual symptoms. Any area of the retina, a delicate layer of tissue on the interior the posterior wall of the human eye, can be impacted by retinal disorders. Optical coherence tomography (OCT) is the utmost commonly used imaging procedure for diagnosing retinal disorders such as age-related macular degeneration (ARMD), diabetic retinopathy, pigment epithelial detachment (PED), macular holes, and more. In this study, we put forth a brand-new technique for accurately extracting features from OCT images to identify PED diseases. For the preprocessing step, we examined the wiener filtering method. After that, we segmented the retinal pigment epithelium (RPE) layer used to the thresholding method, extracted the features from the RPE layer, and then gave the features to machine learning (ML) classifiers like the support vector machine (SVM), logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), naive Bayes (NB), and artificial neural network (ANN). The total dataset about 200 images among 100 is normal and 100 is PED, we trained the dataset as an unbalanced and balanced group. The RF is the best outcome in comparison of other classifiers. The overall outcome of random forest is 100% accuracy.
Volume: 15
Issue: 2
Page: 1572-1583
Publish at: 2025-04-01

Data analysis and visualization on titanic and student’s performance datasets-an exploratory study

10.11591/ijict.v14i1.pp68-76
Seong-Cheol Kim , Surender Reddy Salkuti , Alka Manvayalar Suresh , Madhu Sree Sankaran
Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
Volume: 14
Issue: 1
Page: 68-76
Publish at: 2025-04-01

Explainable zero-shot learning and transfer learning for real time Indian healthcare

10.11591/ijict.v14i1.pp91-101
Swati Saigaonkar , Vaibhav Narawade
Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Volume: 14
Issue: 1
Page: 91-101
Publish at: 2025-04-01

Markov processes in Bayesian network computation

10.11591/ijece.v15i2.pp2181-2191
Assem Shayakhmetova , Nurbolat Tasbolatuly , Ardak Akhmetova , Assel Abdildayeva , Marat Shurenov , Anar Sultangaziyeva
The article examines the influence of Markov processes on computations in Bayesian networks (BN), an important area of research within probabilistic graphical models. The concept of Bayesian Markov networks (BMN) is introduced, an extension of traditional Bayesian networks with the addition of a Markov constraint, according to which the probability in a node can only depend on the state of neighboring nodes. This constraint makes the model more realistic for many practical tasks, as most graphical models that reflect real-world processes possess the Markov property. The article also discusses that Bayesian networks, in the absence of evidence, actually exhibit the Markov property. However, when evidence (additional information) is introduced into the model, challenges arise that require more complex computational methods. In response, the article proposes algorithms adapted for working with Bayesian Markov networks in the presence of evidence. These algorithms are aimed at optimizing computations and reducing computational complexity. Additionally, a comparative analysis of calculations in Bayesian networks without Markov constraints and with them is conducted, highlighting the advantages and disadvantages of each approach. Special attention is paid to the practical applications of the proposed methods and their effectiveness in various scenarios.
Volume: 15
Issue: 2
Page: 2181-2191
Publish at: 2025-04-01

Revolutionizing nonrigid demons registration with the whale optimization algorithm

10.11591/ijece.v15i2.pp2372-2380
Abhisek Roy , Pranab Kanti Roy , Anirban Mitra , Swarnali Daw , Dipannita Basu , Sayan Chakraborty
Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The ground-breaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA-NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOA-which draws inspiration from whale hunting behavior-is integrated into the NDR architecture. The WOA-NDR approach intelligently explores the solution space, enhancing convergence and avoiding premature convergence. With the innovative WOA and NDR integration, the nonrigid image registration process is revolutionized and yields superior outcomes in terms of robustness and accuracy. The efficiency of the suggested strategy is demonstrated by experimental findings on a dataset of monomodal images. The obtained results are also compared with particle swarm optimization (PSO) based framework.
Volume: 15
Issue: 2
Page: 2372-2380
Publish at: 2025-04-01
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