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Telecommunication project methods as an effective tool in modern education

10.11591/ijere.v14i2.31292
Marina Lebedeva , Vladimir Beketov , Marina Taranova
This article is devoted to the study of the problem of insufficient involvement of philology students in the educational process, which negatively affects their academic performance. To solve this problem, a telecommunication project was developed and implemented to improve learning efficiency and student engagement. The research was based on a pilot project with one group, and survey methods were also utilized to measure the level of student engagement in the learning process. Statistical analysis results indicate a noticeable improvement in students’ academic performance after the implementation of the innovative telecommunication course, with the average score increasing from 75.5 to 80.1, supported by a statistically significant level. Survey data on student engagement demonstrate a high level of positive attitude towards the use of technology in the educational process. The overall trend indicates a positive attitude towards new technologies in education. The practical significance of this article lies in highlighting telecommunication project methods as an effective tool in modern education. Applying the results of this research in educational practice can contribute to the development and implementation of new educational programs based on telecommunication projects.
Volume: 14
Issue: 2
Page: 1331-1339
Publish at: 2025-04-01

A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification

10.11591/ijece.v15i2.pp1670-1685
Vinoth Rathinam , Sasireka Rajendran , Valarmathi Krishnasamy
A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
Volume: 15
Issue: 2
Page: 1670-1685
Publish at: 2025-04-01

Finite state machine for retro arcade fighting game development

10.11591/ijict.v14i1.pp102-110
Muhammad Bambang Firdaus , Alan Zulfikar Waksito , Andi Tejawati , Medi Taruk , M. Khairul Anam , Akhmad Irsyad
Traditional fighting games are a competitive genre where players engage in one-on-one combat, aiming to reduce their opponent's health points to zero. These games often utilize two-dimensional (2D) graphics, enabling players to execute various character movements such as punching, jumping, and crouching. This research investigates the effectiveness of the finite state machine (FSM) method in developing a combo system for a retro fighting game, focusing on its implementation within the Godot Engine. The FSM method, which structures game behavior through states, events, and actions, is central to the game's control system. By employing the game development life cycle (GDLC) methodology, this study ensures a systematic and structured approach to game design. Special attention is given to the regulation of the combo hit system for the game's protagonist in Brawl Tale. The research culminates in the successful development of the retro fighting game Brawl Tale, demonstrating that the FSM method significantly enhances the fluidity and responsiveness of character movements. The findings suggest that the FSM method is an effective tool for simplifying and improving gameplay mechanics in retro-style fighting games.
Volume: 14
Issue: 1
Page: 102-110
Publish at: 2025-04-01

Architecture of multi-agent systems for generative automatic matching among heterogeneous systems

10.11591/ijece.v15i2.pp2345-2355
Zouhair Ibn Batouta , Rachid Dehbi , Mohamed Talea
This paper presents the generative automatic matching (GAM) approach, implemented through a multi-agent system (MAS), to address the challenges of heterogeneity across meta-models. GAM integrates automatic meta-model matching with model generation, offering a comprehensive solution to complex systems involving diverse architectures. The key innovation lies in its ability to automate both the detection of correspondences and the transformation of models, improving the precision and recall of matching processes. The system's scalability and adaptability are enhanced by MAS, allowing for efficient management of diverse meta-models. The approach was evaluated through relational to big data UML meta-models (RBDU) case study. The results demonstrated high accuracy, with precision and recall metrics approaching 1, underscoring the robustness of GAM in managing heterogeneous systems. Compared to traditional methods, GAM offers significant advantages, including automated matching and generation, adaptability to various domains, and superior performance metrics. The study contributes to the field of model-driven engineering (MDE) by formalizing a method that effectively bridges the gap between heterogeneous meta-models. Future research will focus on refining matching heuristics, expanding case studies.
Volume: 15
Issue: 2
Page: 2345-2355
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

Planar hexagonal patch multiple input multiple output 4x4 antenna for UWB applications

10.11591/ijict.v14i1.pp174-181
Nasrul Nasrul , Firdaus Firdaus , Nurraudya Tuz Zahra , Maulidya Rachmawati
The combination of Multiple Input Multiple Output (MIMO) antennas and Ultra-Wideband (UWB) technology offers several advantages, including reduced interference, improved isolation, and optimized dual paths. These benefits extend the range and enhance signal quality. However, designing UWB-MIMO antennas presents challenges, such as achieving low mutual coupling for high isolation and creating small-sized antennas suitable for portable devices while being effective for UWB frequencies in a MIMO configuration. The proposed antenna is a 4x4 planar MIMO antenna with a hexagon-shaped patch, a partial ground plane featuring an inverted L-stub on the left side, and a plus-shaped slot in the centre ground. It has dimensions of 32 x 32 x 1.6 mm³ and is capable of achieving a wide bandwidth of 3-12.5 GHz. The antenna's performance measurements are impressive: return loss below -10 dB at frequencies of 3-12.5 GHz, mutual coupling below -16.5 dB, Envelope Correlation Coefficient (ECC) bellow 0.005, Diversity gain of more than 9.97, Total Active Reflection Coefficient (TARC) below -10 dB. Based on these results, the proposed antenna offers excellent performance for UWB applications, featuring high efficiency, minimal interference between antenna elements, and optimal diversity performance.
Volume: 14
Issue: 1
Page: 174-181
Publish at: 2025-04-01

A review of convolutional neural networks for classifying power quality problems using Keras API

10.11591/ijeecs.v38.i1.pp1-21
Adamu Sa'idu , Nasiru B. Kadandani , Bala Boyi Bukata
The major causes of electric power quality (PQ) problems are mainly due to the increased utilization of nonlinear loads, capacitor and load switching events, transformer energization, and occurrence of assorted faults at the distribution corridor. The problems often introduce harmonics and other waveform anomalies like voltage sags, voltage swells and interruptions along the power systems. A timely classification of such problems is important in understanding their impact on costly power system economy. The paper explores comprehensive review of PQ issues, operational concept of convolutional neural network (CNN) and its utilization in solving PQ problems. Novel deep learning (DL) approach using variant of DenseNet CNN technique in Keras API platform is deployed to extract the features of, and classify PQ problems. The proposed technique improves classification performance with an accuracy of 99.96%. It shows remarkable improvement over the traditional techniques in the literature which were 73.53% to 99.92% accurate for a period from 2018 to 2023. The most promising part of the method is the improvement shown in the classification performance when compared with that obtained in the literature. The technique can also be applied in real time to cater for real PQ problems.
Volume: 38
Issue: 1
Page: 1-21
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

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

Challenges, opportunities, and effects of alternative assessment approaches in teaching practices: a systematic literature review

10.11591/ijere.v14i2.32283
Najdah Sanusi , Hafizhah Zulkifli , Mohd Isa Hamzah
Alternative assessment, encompassing methods such as portfolios, project-based evaluations, and peer assessments, aligns with 21st-century student-centered learning goals by holistically and authentically evaluating students’ progress. This research applies the systematic review literature method with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The study analyzed 47 peer-reviewed articles published between 2019 and 2023, sourced from Web of Science, Scopus, and Education Resource Information Centre (ERIC) databases. The thematic analysis revealed three main themes: i) challenges in implementing alternative assessments, including teacher readiness and assessment skills; ii) opportunities for enhancing creativity and variety in teaching approaches; and iii) positive effects on teachers’ motivation and understanding of student learning. The findings highlight the need for targeted professional development to support teachers in effectively implementing alternative assessments. This review contributes to the growing body of knowledge on innovative assessment practices and their impact on teaching and learning in contemporary educational settings.
Volume: 14
Issue: 2
Page: 1105-1113
Publish at: 2025-04-01

The integration of discrete contourlet transform in OFDM framework for future wireless communication

10.11591/ijict.v14i1.pp182-194
Mohamed Hussien Mohamed Nerma , Adam Mohamed Ahmed Abdo
In the upcoming era, the forthcoming sixth-generation (6G) wireless communication network will demand highly efficient technology to support extensive capacity, ultra-high speeds, low latency, scalability, and adaptability. While the current fifth-generation (5G) wireless communication system relies on OFDM technology, the evolution towards a beyond 5G wireless communication system necessitates a new OFDM framework. This study introduces a novel OFDM system that integrates the discrete Contourlet transform. A comparative analysis has been conducted among the proposed system, conventional OFDM, and curvelet-based OFDM systems. The results indicate that the proposed system offers improvements in bit error rate (BER), reduced computational complexity, decreased peak-to-average power ratio (PAPR), and enhanced power spectrum density (PSD) when contrasted with both the traditional and curvelet-based systems.
Volume: 14
Issue: 1
Page: 182-194
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

Maximum expansion with contiguity constraints scheduling algorithm: enhancing uplink transmission in long-term evolution vehicular environments

10.11591/ijece.v15i2.pp1709-1719
Shafinaz Ismail , Shajahan Maidin , Darmawaty Mohd Ali , Mohd Kamarulnizam Abdul Rahim
Uplink scheduling has become increasingly important due to increased activities like uploading videos, photos, and file sharing. Many users share or stream live videos and engage with social networks, significantly increasing uplink data traffic volume. Single carrier frequency division multiple access (SC-FDMA) is favored for its power efficiency and high data rates, benefiting user equipment (UE) battery life. However, maintaining the contiguity of resource blocks (RBs) poses challenges in uplink scheduling. The maximum expansion with contiguity constraints (MECC) algorithm has been introduced to address this challenge. MECC prioritizes contiguity, fairness, and throughput for users at the cell edge. The algorithm operates in two phases: initially allocating RBs proportionally and assigning RBs with the highest metrics while ensuring contiguity. Performance evaluation of MECC, conducted under conditions simulating vehicular movement at 30 km/h, demonstrates its superiority over other algorithms. MECC provides high fairness and throughput for both real-time (RT) and non-real-time (NRT) traffic, making it the preferred scheduler for ensuring quality of service (QoS) for both traffic types. Its focus on contiguity, fairness, throughput, and spectral efficiency establishes MECC as a valuable tool for optimizing uplink transmission in mobile networks, addressing the evolving needs of users in today's digital landscape.
Volume: 15
Issue: 2
Page: 1709-1719
Publish at: 2025-04-01

Learning high-level spectral-spatial features for hyperspectral image classification with insufficient labeled samples

10.11591/ijai.v14.i2.pp1211-1219
Douglas Omwenga Nyabuga , Godfrey Nyariki
Hyperspectral image (HSI) classification research is a hot area, with a mass of new methods being developed to improve performance for specific applications that use spatial and spectral image material. However, the main obstacle for scientists is determining how to identify HSIs effectively. These obstacles include an increased presence of redundant spectral information, high dimensionality in observed data, and limited spatial features in a classification model. To this end, we, therefore, proposed a novel approach for learning high-level spectral-spatial features for HSI classification with insufficient labeled samples. First, we implemented the principal component analysis (PCA) technique to reduce the high dimensionalities experienced. Second, a fusion of 2D and 3D convolutions and DenseNet, a transfer learning network for feature learning of both spatial-spectral pixels. The achieved experimental results are comparatively satisfactory to contrasted approaches on the widely used HSI images, i.e., the University of Pavia and Indian Pines, with an overall classification accuracy of 97.80% and 97.60%, respectively.
Volume: 14
Issue: 2
Page: 1211-1219
Publish at: 2025-04-01

Adaptation and validation of academic resilience scale in Bengali

10.11591/ijere.v14i2.30113
Riya Ahmed , Bijoy Krishna Panda , Muktipada Sinha
The purpose of the current study was to adapt and validate the Academic Resilience Scale (ARS-30) in the context of West Bengal and other Bengali-speaking regions. The research included a total of 628 participants. The data analysis occurred in three stages. Initially, confirmatory factor analysis was employed to assess the factorial validity of the Bengali version of ARS-30 scale, revealing a poor fit for the original three-factor model. Subsequently, further exploratory factor analysis (EFA) suggested a more suitable two-factor structure. In the third stage, this newly derived two-factor structure was validated through confirmatory factor analysis (CFA) with an independent sample. The adapted scale, renamed ARS-19, measures two factors related to academic resilience: negative affect and emotional response (6 items) and positive adaptation (13 items). Results from validity and reliability analyses indicated that the ARS-19 is a valid and reliable tool for assessing academic resilience in the aforementioned context. This study contributes to the literature by proposing a valid and reliable academic resilience measurement for West Bengal as well as other Bengali-speaking regions, facilitating practitioners in assessing academic resilience among higher education students.
Volume: 14
Issue: 2
Page: 947-960
Publish at: 2025-04-01
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