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Evaluation of the performance of mobile telephone networks: literature review

10.11591/ijeecs.v41.i1.pp128-139
Pascal Valandi , Djorwe Temoa , Nsouandele Jean Luc , Tsama Eloundou Pascal , Dokrom Froumsia
Improving the quality of service (QoS) of telephone networks inevitably involves studying previous work on the evaluation of its performance indicators. Several researchers have addressed the subject of evaluating the performance of service of mobile telephone networks. Some proceeded through user surveys and others opted for more objective methods using either professional scanners or developed: hyper text markup language (HTML) or Android applications. The results show that whether by subjective or objective methods, this work has made it possible to advance research and allow other researchers to progress further in the process of evaluating mobile networks. In this study which constitutes a review of the literature, we investigated the different approaches, methods, and most recent results mentioned by researchers to evaluate the QoS by relying much more on objective evaluation. Despite the advances and their limits, in our proposal we intend to rely on data sciences through their tools to evaluate the QoS with more precision.
Volume: 41
Issue: 1
Page: 128-139
Publish at: 2026-01-01

An enhanced NLP approach for BI-RADS extraction in breast ultrasound reports using deep learning

10.11591/ijeecs.v41.i1.pp191-199
Ahmed Sahl , Shafaatunnur Hasan , Maie M. Aboghazalah
Breast cancer stands as one of the top causes of death around the globe, making the accurate interpretation of breast ultrasound reports vital for early diagnosis and treatment. Unfortunately, key findings in these reports are often buried in unstructured text, complicating automated extraction. This study presents a deep learning-based natural language processing (NLP) approach to extract breast imaging reporting and data system (BI-RADS) categories from breast ultrasound data. We trained a recurrent neural network (RNN) model, specifically using a BiLSTM architecture, on a dataset of reports that were manually annotated from a hospital in Saudi Arabia. Our approach also incorporates uncertainty estimation techniques to tackle ambiguous cases and uses data augmentation to boost model performance. The experimental results indicate that our deep learning method surpasses traditional rule-based and machine-learning techniques, achieving impressive accuracy in classification tasks. This research plays a significant role in automating radiology reporting, aiding clinical decision-making, and pushing forward the field of breast cancer research.
Volume: 41
Issue: 1
Page: 191-199
Publish at: 2026-01-01

Design and construction of an Arduino-based baby incubator simulator using IoT

10.11591/ijeecs.v41.i1.pp99-108
Liza Rusdiyana , Joel Juanda Jamot Damanik , Bambang Sampurno , Suhariyanto Suhariyanto , Mahirul Mursid , Ika Silviana Widianti
This study aims to create a baby incubator simulator equipped with an internet of things (IoT)-based temperature control system using Arduino UNO. We use a DHT22 sensor to measure temperature and humidity, as well as fuzzy logic to ensure more accurate and responsive temperature control. The Thinger.io platform enables real-time monitoring and control of the incubator, providing flexibility and ease of supervision. With fuzzy logic, the temperature control system can handle changes and uncertainties in the incubator environment, providing a smoother response compared to traditional on-off methods. Testing shows that this system has a very low error rate, with an error value of only 0.97%, meaning that the measured temperature is almost identical to the actual conditions inside the incubator. Additionally, the authors used mice as a model for premature infants in the testing. The results showed that the mice's body temperature increased gradually and stably in line with the incubator conditions, reaching the desired temperature within 90 minutes. This demonstrates that our temperature control system is capable of maintaining optimal environmental conditions for premature infants.
Volume: 41
Issue: 1
Page: 99-108
Publish at: 2026-01-01

Artillery fire control based on artificial intelligence algorithm of unmanned aerial vehicle

10.11591/ijeecs.v41.i1.pp83-89
Azad Agalar Bayramov , Samir Suleyman Suleymanov , Fatali Nariman Abdullayev
The article presents the developed artillery fire remote control complex using unmanned aerial vehicles (UAVs) based on an artificial intelligence (AI) algorithm. The developed complex for artillery fire control includes sensor modules for assessing the environment, collecting and processing information, planning and decision-making, and developing a command for the commander of an artillery battalion, division, or brigade. The main advantage of the developed artillery fire control system using UAVs based on an AI algorithm is the most rapid decision-making without human intervention, based on a quick assessment of the environment, the type of enemy weapons, and their category of importance, and an assessment of the distance to the enemy’s military arms. An algorithm is proposed to minimize the power of artillery fire to suppress the enemy.
Volume: 41
Issue: 1
Page: 83-89
Publish at: 2026-01-01

Convolutional neural network DenseNet in classifying dyslexic handwriting images

10.11591/ijeecs.v41.i1.pp220-232
Chelsea Zaomi Pondayu , Widodo Widodo , Murien Nugraheni
Dyslexia is a specific learning disability (SLD) associated with word-level reading difficulties and often manifests in childhood handwriting through irregular spacing and inconsistent letter sizing, due to shared phonological and orthographic processing. Early identification is critical; however, traditional diagnostic procedures are time-consuming and unsuitable for large-scale screening. This study aimed to develop a handwriting analysis at the paragraph-level using a DenseNet121 convolutional neural network (CNN) model as a low-cost dyslexia screening tool for resource-constrained educational settings. One hundred English handwriting images were preprocessed and standardized into two hundred samples, with 70% of the dataset evaluated using 4-fold cross-validation and the remaining 30% used for testing. The model achieved 90% test accuracy and 92.86% training accuracy, significantly outperforming a random forest baseline that reached 83.57% train accuracy and 63.33% test accuracy, with statistical significance confirmed by McNemar’s test. The main contribution of this study is the demonstration that a lightweight, single-architecture DenseNet121 using paragraph-level analysis can achieve competitive performance compared to prior studies that relied on more complex hybrid models and character-level analysis, while requiring substantially lower computational resources and simplified pipeline. These findings indicate that DenseNet121 provides a robust and low-cost solution for preliminary dyslexia screening in resource-limited educational environments.
Volume: 41
Issue: 1
Page: 220-232
Publish at: 2026-01-01

Ultra-high isolation dual-port circular patch antenna at 2.4 GHz

10.11591/ijeecs.v41.i1.pp140-152
Meriem Boucif , Fayza Bousalah , Hayat Benosman
Reliable wireless communication in the 2.4 GHz industrial, scientific, and medical band increasingly relies on antenna systems that can provide high inter-port isolation in multiple-input multiple-output (MIMO) configurations. This paper presents a circular microstrip patch antenna and its extension to a dual-port MIMO configuration designed for 2.4 GHz operation. The antenna is implemented on a low-loss substrate and evaluated using full-wave electromagnetic simulations to assess impedance matching, radiation performance, and MIMO diversity metrics. To enhance inter-port isolation in the array, an inverted U-shaped defected ground structure (DGS) is introduced between the two radiating elements. The optimized design achieves excellent matching around 2.4 GHz and ultra-high isolation of approximately -78.7 dB, while maintaining stable gain and radiation patterns across the operating band. These results indicate that the proposed antenna offers a simple and effective solution for compact, energy-efficient, and robust 2.4 GHz MIMO front ends in internet of things (IoT) and other shortrange wireless communication systems.
Volume: 41
Issue: 1
Page: 140-152
Publish at: 2026-01-01

Cyber physical systems maintenance with explainable unsupervised machine learning

10.11591/ijeecs.v41.i1.pp300-308
V. Durga Prasad Jasti , Koudegai Ashok , Ramarao Gude , Prabhakar Kandukuri , Surendra Nadh Benarji Bejjam , Anusha B.
As cyber-physical systems (CPS) continue to play a pivotal role in modern technological landscapes, the need for robust and transparent machine learning (ML) models becomes imperative. This research paper explores the integration of explainable artificial intelligence (XAI) principles into unsupervised machine learning (UML) techniques for enhancing the interpretability and understanding of complex relationships within CPS. The key focus areas include the application of self-organizing maps (SOMs) as a representative unsupervised learning algorithm and the incorporation of interpretable ML methodologies. The study delves into the challenges posed by the inherently intricate nature of CPS data, characterized by the fusion of physical processes and digital components. Traditional black-box approaches in unsupervised learning often hinder the comprehension of model-generated insights, making them less suitable for critical CPS applications. In response, this research introduces a novel framework that leverages SOMs, a powerful unsupervised technique, while concurrently ensuring interpretability through XAI techniques. The paper provides a comprehensive overview of existing XAI methods and their adaptation to unsupervised learning paradigms. Special emphasis is placed on developing transparent representations of learned patterns within the CPS domain. The proposed approach aims to enhance model interpretability through the generation of human-understandable visualizations and explanations, bridging the gap between advanced ML models and domain experts.
Volume: 41
Issue: 1
Page: 300-308
Publish at: 2026-01-01

Comparative analysis of linear regression, random forest, and LightGBM for hepatitis disease prediction

10.11591/ijeecs.v41.i1.pp430-438
Hennie Tuhuteru , Goldy Valendria Nivaan , Marvelous Marvel Rijoly , Joselina Tuhuteru
In bioinformatics research, computational pattern-analysis techniques are frequently employed to assist in disease prediction and diagnostic modeling, including applications for hepatitis prognosis. Hepatitis is a type of serious disease with various types that have the potential to threaten the life of the sufferer without showing significant symptoms and signs, so many sufferers do not realize that they are affected by the disease. Various methods are used to predict diseases in the hope of providing the best results from the learning model used. The objective of this study is to implement linear regression, random forest, and light gradient boosting machine (LightGBM) to estimate mortality risk among hepatitis patients. In addition, a performance comparison of the results of hepatitis disease prediction using the three algorithms was also carried out to find out which model gave the most accurate and optimal results. The results of this study show that the application of learning models from the linear regression, random forest and Light-GBM algorithms has been successfully carried out to predict the survival status of patients with hepatitis. The findings reveal that random forest achieved the highest predictive performance with an accuracy of 84%, followed by LightGBM at 77% and linear regression at 32%.
Volume: 41
Issue: 1
Page: 430-438
Publish at: 2026-01-01

Detection of COVID-19 using chest X-rays enhanced by histogram equalization and convolutional neural networks

10.11591/ijeecs.v41.i1.pp387-393
Nazif Tchagafo , Abderrahmane Ez-Zahout , Ahiod Belaid
The persistent global health crisis initiated by the COVID-19 pandemic continues to demand robust and high-throughput diagnostic solutions. While gold-standard methods, such as polymerase chain reaction (PCR) testing, are accurate, their scalability and turnaround time remain limitations in high volume settings. This paper introduces a novel deep learning framework designed for rapid and accurate detection of COVID-19 from chest X-ray (CXR) imagery. Our methodology leverages a convolutional neural network (CNN) architecture, augmented by a crucial pre-processing stage: histogram equalization. This step is vital for enhancing the subtle contrast features inherent in CXR scans, there by significantly improving the quality of the input data and facilitating superior feature extraction by the CNN. The model was trained and rigorously validated on a dedicated dataset. Performance was systematically quantified using a comprehensive confusion matrix, yielding key metrics such as precision and specificity, alongside the receiver operating characteristic (ROC) curve. The achieved results are highly encouraging, demonstrating a classification accuracy of 98.45%. This innovative approach offers a substantial acceleration of the diagnostic process, providing a non-invasive and highly effective complementary tool for clinicians. Ultimately, this advancement has the potential to streamline patient management protocols and alleviate diagnostic pressures on global healthcare infrastructures.
Volume: 41
Issue: 1
Page: 387-393
Publish at: 2026-01-01

Invisible watermarking as an additional forensic feature of e-meterai

10.11591/ijeecs.v41.i1.pp344-356
H.A Danang Rimbawa , Sirojul Alam , Joko W. Saputro , Teddy Mantoro
The e-meterai is an official digital product of the Indonesian government issued by the Directorate General of Taxation (DGT). Its usage has become increasingly widespread as conventional documentation transitions to digital formats, serving the same function as its printed counterpart. This product features a quick-response code embedded with unique Indonesian codes and offers overt, covert, and forensic features. This study aims to experiment with adding a forensic feature in the form of an invisible watermark. We employed two watermark embedding techniques, discrete Fourier transform (DFT) and scale-invariant feature transform (SIFT), to determine which is more suitable for this application. After embedding the watermark, we also simulate various attacks including gaussian noise, salt and pepper noise, averaging filter, rotation, translation, and speckle noise. For each attack, we calculated with normalized-cross correlation (NCC) values, obtaining 0.863 and 0.976 for the gaussian noise attack, 0.929 and 0.984 for the salt and pepper attack, 0.975 and 0.984 for the averaging filter attack, 0.173 and 0.097 for rotation attacks, 0.172 and 0.032 for translation attack, and 0.972 and 0.996 for speckle noise attack, using DFT and SIFT techniques, respectively.
Volume: 41
Issue: 1
Page: 344-356
Publish at: 2026-01-01

YOLOv8m enhancement using α-scaled gradient-normalized sigmoid activation for intelligent vehicle classification

10.11591/ijeecs.v41.i1.pp153-167
Renz Raniel V. Serrano , Jen Aldwayne B. Delmo , Cristina Amor M. Rosales
Vehicle classification plays a vital part in the development of intelligent transportation systems (ITS) and modern traffic management, where the ability to detect and identify vehicles accurately in real time is essential for maintaining road efficiency and safety. This paper presents an enhancement to the YOLOv8m model by refining its activation function to achieve higher accuracy and faster response in diverse traffic and environmental situations. In this study, two alternative activation functions—Mish and Swish—were integrated into the YOLOv8m structure and tested against the model’s default sigmoid linear unit (SiLU). Training and evaluation were carried out using a comprehensive dataset of vehicles captured under different lighting and weather conditions. The experimental findings show that the modified activation design leads to better model convergence, improved generalization, and a noticeable boost in detection performance, recording up to 5.4% higher accuracy and 6.6% better mAP scores than the standard YOLOv8m. Overall, the results confirm that fine-tuning activation behavior can make deep learning models more adaptive and reliable for vehicle classification tasks in real-world intelligent transportation environments.
Volume: 41
Issue: 1
Page: 153-167
Publish at: 2026-01-01

Cryptojacking detection using model-agnostic explainability

10.11591/ijeecs.v41.i1.pp394-408
Elodie Ngoie Mutombo , Mike Wa Nkongolo , Mahmut Tokmak
Cryptojacking is the illicit use of computing resources for cryptocurrency mining. It has emerged as a serious cybersecurity threat that degrades critical system performance and increases operational costs. This paper proposes an advanced machine learning (ML) framework that integrates transformer based language models with post hoc explainable artificial intelligence (XAI) to detect cryptojacking using complementary network traffic and process memory data. Numerical and categorical features are discretized and tokenized to enable semantic modelling and contextual learning. Experimental results show that transformer models effectively capture cryptojacking-related behavioral patterns, with decoding-enhanced BERT with disentangled attention (DeBERTa) achieving high detection performance and recall exceeding 80%. bidirectional encoder representations from transformers (BERT) attains comparable recall with lower computational overhead, making it well suited for real-time environments, while robustly optimized BERT approach (RoBERTa) and DeBERTa are more appropriate for offline or batch-based analysis. Model performance is evaluated using standard classification metrics, and XAI techniques provide interpretable insights into feature relevance, supporting transparent and reliable detection. In general, the proposed framework delivers an effective and deployment-ready solution for cryptojacking detection.
Volume: 41
Issue: 1
Page: 394-408
Publish at: 2026-01-01

Lossy ECG signal compression based on RR intervals detection with wavelet transform and optimized run-length encoding

10.11591/ijeecs.v41.i1.pp109-118
Nabil Boukhennoufa , Messaoud Garah
It is expensive to transmit or store significant amounts of electrocardiogram (ECG) records, particularly when using telecommunications channels that charge according to the volume of transferred data. The advancement of telemedicine renders compressing ECG signals even more necessary. Compression aims to reduce the size of data while maintaining the features of ECG signals. This paper presents a novel strategy for compressing ECG signals based on 3D format conversion. After identifying the RR intervals, we divide the signal into cardiac cycles and proceed with the cut and align process. A 3D discrete wavelet transform (DWT) is employed to minimize the correlation existing between two adjacent voxels. Moreover, an optimized run-length encoding (RLE), a novel lossless compression technique, has been proposed to increase the compression ratio (CR). The proposed strategy is applied to different types of ECG records of the Arryyhmia database. This algorithm demonstrates improved performance in terms of CR and percentage root-mean-square difference (PRD) compared to several recently published works.
Volume: 41
Issue: 1
Page: 109-118
Publish at: 2026-01-01

Relationship between voltage and resistance in hybrid nanoconductive ink on different substrates in wet and dry conditions

10.11591/ijeecs.v41.i1.pp18-32
Norashikin Shari , Nurfaizey Abd Hamid , Chonlatee Photong , Alan J. Watson , Mohd Azli Salim
Hybrid graphene nanoplatelet/silver (GNP/Ag/SA) conductive inks are increasingly used in flexible electronics, yet there is limited understanding of how substrate type, solvent composition, and moisture exposure jointly control the electrical performance on metal and polymer substrates. This work aims to clarify how terpinol content (5T, 10T, 15T) and substrate properties of copper (Cu), polyethylene terephthalate (PET), and thermoplastic polyurethane (TPU) influence voltage, resistance, and resistivity of screen-printed GNP/Ag/SA tracks under dry and postimmersion wet conditions. GNP/Ag/SA inks were formulated with fixed butanol and varied terpinol contents, printed on Cu, PET, and TPU, and characterized using electrical measurements, adhesion evaluation, and microstructural observations to relate resistivity trends to morphology, surface energy, and hygroscopic behavior. The Cu substrate showed the best performance, with Cu 10T achieving the lowest dry resistivity of approximately 1.2×10-5 Ω.m and Cu 15T the lowest wet resistivity of approximately 2.0×10-5 Ω.m, supported by dense, well-adhered microstructures. The PET exhibited higher resistivity values up to about 10-3 Ω.m and clear degradation after water immersion, while TPU showed very high or unmeasurable resistivity in wet conditions caused by severe ink loss and hygroscopic swelling, highlighting the important role of substrate surface energy and moisture response in determining the reliability of GNP/Ag/SA inks for applications in humid or wet environments.
Volume: 41
Issue: 1
Page: 18-32
Publish at: 2026-01-01

A hybrid divisive K-means framework for big data–driven poverty analysis in Central Java Province

10.11591/ijeecs.v41.i1.pp258-269
Bowo Winarno , Budi Warsito , Bayu Surarso
Clustering is essential in big data analytics, especially for partitioning high dimensional socioeconomic datasets to support interpretation and policy decisions. While K-Means is widely used for its simplicity and scalability, its strong sensitivity to initial centroid selection often leads to unstable results and slower convergence. Previous hybrid approaches, such as Agglomerative–K-Means, attempted to address this issue by using hierarchical clustering for centroid initialization; however, these methods rely on bottom-up merging, which can produce suboptimal initial partitions and increase computational overhead for larger datasets. To overcome these limitations, this study proposes a hybrid divisive–K-Means (DHC) model that employs top-down hierarchical splitting to generate more coherent initial centroids before refinement with K-Means. Using a multidimensional poverty dataset from Central Java Province provided by the Indonesian Central Bureau of Statistics (BPS), the performance of DHC was evaluated against standard K-Means and Agglomerative–K-Means. The assessment included execution time, convergence iterations, and cluster validity indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz). Experimental results demonstrate that DHC reduces execution time by up to 97% and requires 40% fewer iterations than standard K-Means, while achieving comparable or improved cluster quality (e.g., CH Index increasing from 14.3 to 15.8). These findings indicate that the DHC model offers a more efficient and stable clustering solution, addressing the shortcomings of previous standard K-Means methods and improving performance for large-scale socioeconomic data analysis.
Volume: 41
Issue: 1
Page: 258-269
Publish at: 2026-01-01
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