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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,439 Documents
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A hybrid content and character feature method for SMS spam detection Gertrude Selase Gosu; Edward Appau Nketiah; Li Wang; Joshua Fenuku; Xiaoya Xu
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2061-2073

Abstract

Short message service (SMS) spam remains a significant challenge due to its impact on user security and communication efficiency. This study proposes a hybrid spam detection model, convolutional neural network with content and character-based features (CNN-CCB), which integrates word-level features and character-level features, with handcrafted content-based and character-based features in a unified deep learning framework. Unlike conventional CNN and hybrid models that rely primarily on learned representations, the proposed approach incorporates structural features to enhance detection of short and noisy text patterns. Firstly, the text data were tokenized and processed through dual convolutional branches, while handcrafted features are fused to improve classification. Secondly, class weighting is applied to address data imbalance while maintaining predictive reliability. Finally, regularization techniques are employed to prevent overfitting. The experimental results show that CNN-CCB achieves high performance, with an accuracy of 0.997, precision of 0.988, recall of 0.998, and F1-score of 0.993, outperforming baseline models such as long short-term memory (LSTM) dan gated recurrent unit (GRU). The model demonstrates consistent performance across three datasets, indicating the model’s robustness and generalizability. These findings suggest that the proposed hybrid framework is effective for SMS spam detection and has potential applications in mobile security and real-time communication systems.
Spatial and channel attention mechanism for speech disfluency detection using deep learning technique Kusuma H. R.; G. Seshikala
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2106-2119

Abstract

Stuttering is a speech communication disorder, it is characterized by repetitions, prolongation, and unusual pauses that cause interference with the natural flow of speech. In recent times, automatic speech recognition and speech processing systems have gained enormous attention because they are used in most of the human machine interaction applications. However, the performance of these systems is affected by stutter speech, stutter detection is the major challenge due to speech disfluencies. To address this major challenge, this paper introduced a novel deep learning (DL) based paradigm, which integrates a hybrid feature extraction algorithm, with the Spatial and Channel attention mechanism to refine the features and for reliable detection of speech disfluency. This study is conducted on multiple stutter data set which includes UCLASS (Release 1, Release 2), FluencyBank and SEP-28k. The major drawback of all these data sets is data imbalance. To reduce this imbalance, the author used data augmentation techniques, which includes, noise, music, reverberation and pitch shifting methods. However, increasing the stutter detection accuracy remains a challenging issue. To address this issue, the author proposed a hybrid feature extraction model, which extracts temporal, contextual, spectral, and pitch information from the speech signal. The obtained features are then processed through the attention mechanism where channel and spatial attention models help to refine the features. Finally, a multiclass convolutional neural network (CNN) classifier is used to detect the stutter event in the speech signals. The results show that our model with spatial and channel attention mechanism performs better than existing deep learning approaches and accurately detects stuttering.
Automated prediction of the mode of birth delivery using geometric features of uterine contraction segments Rubana Hoque Chowdhury; Roma Sultana; Quazi Delwar Hossain; Mohiuddin Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1885-1898

Abstract

Pregnancy is a unique, complex process and it's hard to predict the mode of birth delivery due to the lack resources. This study aims to develop a clinical decision support system to predict birth delivery mode according to three categories: spontaneous vaginal delivery, induced vaginal delivery, and cesarean section. This methodology entails the automated extraction of uterine contraction segments from the electrohysterogram (EHG) signal based on the zero-crossing rate. Each segment is processed through a discrete Fourier transform to obtain the Fourier coefficients. Geometric features, including area, perimeter, circularity, variance, and bending energy were extracted from the boundary shape of these complex coefficients using the convex hull method. We found the women who experience spontaneous deliveries exhibit higher feature values and a lower circularity value compared to those who undergo cesarean sections or induced births. Based on these extracted parameters, the random forest (RF) model yielded promising results: reaching an accuracy above 90% in the classification between caesarean and spontaneous deliveries and spontaneous and induced vaginal deliveries and somewhat lower, around 70% between induced and cesarean. To conclude, the utilization of all proposed EHG parameters through machine learning can enhance obstetricians' ability to predict the mode of birth delivery.
A deep learning-driven traveling wave method for GPS-free and noise-resilient fault location in compensated power networks Asma Talbi; Abdehafid Bayadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1704-1723

Abstract

This paper introduces a novel hybrid fault location technique for high-voltage transmission lines, integrating travelling wave (TW) principles, discrete wavelet transforms (DWT), and long short-term memory (LSTM) neural networks. The proposed method enhances fault detection speed, improves location accuracy, and demonstrates resilience against high-impedance faults. The LSTM network is specifically trained to detect the arrival of the initial wavefront through single-ended measurements, while DWT effectively extracts the high-frequency components of transient signals. A simulation of a 400 kV, 120 km transmission line, modeled on real parameters from the Algerian grid, was conducted using ATP-EMTP. The methodology was implemented in MATLAB and compared with several state-of-the-art approaches, including GPS-synchronized TW methods, under various noise conditions with signal-to-noise ratios (SNR) as low as 5 dB. Additionally, the influence of thyristor-controlled series compensators (TCSC) on location accuracy was explored. The results confirm the applicability of the proposed technique in modern wide-area protection schemes, especially for remote relays and next-generation digital fault recorders (DFRs).
A comparative study of baseline convolutional neural network and ResNet50 for image-based tomato leaf disease classification Sumana Budsabok; Wachiraporn Polpanumas; Piyanan Khongphai
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1876-1884

Abstract

Image-based techniques are widely used in plant disease classification to support agricultural productivity and facilitate early detection. This study presents a comparative analysis between a baseline convolutional neural network (CNN) and a ResNet50-based transfer learning model for tomato leaf disease classification. A publicly available dataset containing five categories—tomato bacterial spot, tomato late blight, tomato septoria leaf spot, tomato yellow leaf curl virus, and healthy leaves—was used in the experiments. Model performance was evaluated using several standard classification metrics, with emphasis on overall accuracy. The baseline CNN achieved an accuracy of 97.0%, whereas the ResNet50 model reached 99.6%. The results demonstrate that the ResNet50 model produces more stable and reliable predictions, particularly when distinguishing between visually similar disease classes. These findings confirm that transfer learning can effectively improve classification performance in plant disease recognition tasks.
Emotion-conditioned podcast recommendation using context-aware collaborative filtering Nova Noor Kamala Sari; Viktor Handrianus Pranatawijaya; Efrans Christian; Dina Meiliana
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2182-2191

Abstract

The rapid growth of podcast consumption on video-based platforms introduces new challenges for recommender systems, as user media choices are influenced not only by historical preferences but also by dynamic psychological states. Conventional approaches primarily model similarity and often ignore emotional context, particularly in long-form media. This study investigates emotion-conditioned recommendation behavior by treating user emotion as a contextual variable within a hybrid recommendation framework. This framework models emotion as a conditioning mechanism that constrains candidate selection prior to collaborative ranking. A dataset of 4,468 YouTube podcast transcripts was collected and preprocessed. Emotional labels were generated using the NRC emotion lexicon and validated through manual verification. User emotional state was detected using a support vector machine (SVM), while implicit preferences were estimated from engagement indicators using a random forest (RF) model. The recommendation stage integrates contextual pre-filtering based on Plutchik’s emotional relationships with collaborative filtering using k-nearest neighbors (KNN) with cosine similarity. Evaluation using stratified 5-fold cross-validation, baseline comparison, and Wilcoxon signed-rank testing shows that emotional context alters recommendation ranking behavior and improves ranking quality and retrieval coverage within the candidate space. These findings indicate that emotion acts as a conditioning mechanism in long-form media consumption, influencing recommendation outcomes beyond predictive accuracy.
Application of predictive analytics and its impact on strategic decision-making in financial institutions in Peru Anibal Gabriel Quintana Escobedo; Percy Junior Castro Mejía
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp2158-2168

Abstract

This study evaluates the impact of implementing Machine Learning architectures on strategic decision-making within financial institutions in Metropolitan Lima. Using an experimental approach, models such as XGBoost were developed, achieving an AUC-ROC of 0.92 and an F1-Score of 0.89. The results demonstrate a significant improvement in operational indicators between 2024 (pre-intervention) and 2025 (post-intervention). Specifically, credit delinquency decreased from 4.8% to 4.2%, while fraud losses dropped by 25% (from 0.8% to 0.6%). Additionally, the credit approval time was reduced from 15 to 5 days, contributing to an 18.2% increase in return on assets (ROA). The findings confirmed that transitioning from reactive to proactive predictive models optimizes operational efficiency and financial resilience. This research provides a technical and strategic roadmap for the digital transformation of the banking sector through advanced analytics and a data-driven culture.
From data to intelligence: foundations of learning systems, representation, and computational perception Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1669-1676

Abstract

The rapid evolution of intelligent systems has shifted the focus of electrical and computer engineering from isolated data processing toward integrated models of machine cognition. This editorial introduces a foundational perspective on machine intelligence systems, emphasizing the transformation from raw data to meaningful intelligence through learning systems, representation mechanisms, and computational perception. In contemporary AI-driven environments, intelligence is no longer defined solely by algorithmic performance, but by the ability to construct structured representations of the world and interpret complex multimodal signals. Learning systems, particularly those grounded in machine learning and deep learning paradigms, serve as the core mechanism enabling this transformation. Representation learning provides the bridge between unstructured data and abstract knowledge, while computational perception enables machines to interpret visual, auditory, and sensor-based information in real time. Together, these components form the foundational architecture of intelligent systems that underpin emerging applications in engineering, automation, and cyber-physical environments. This editorial sets the stage for understanding intelligence as an emergent computational construct, highlighting its role as the first phase in the broader cognitive intelligence systems continuum that progresses toward adaptive, autonomous, and socio-cognitive systems in future research directions.
Challenges and prospects in the 6G-enabled Internet of Things ecosystem Mina Asaduzaman; Ferdous Hossain; Tan Kim Geok
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1927-1943

Abstract

The exponential growth of the Internet of Things (IoT) applications is revealing critical limitations in current fifth generation networks (5G), especially with respect to scalability, latency, energy efficiency and intelligent resource management. These concerns make sixth-generation (6G) communication systems a near-future solution to facilitate intelligent, autonomous, and sustainable IoT ecosystems by amalgamating artificial intelligence (AI), terahertz (THz) communication, edge intelligence, semantic communication, and ultra-reliable low-latency communication (URLLC). But the research environment regarding 6G enabled IoT is still fragmented with the non-existence of a common analytical framework. In this paper, we present a structured survey and taxonomy based analytical framework for the 6G enabled IoT ecosystem. It classifies the recent works into enabling technologies, intelligent architectures, emerging applications, deployment challenges and future research directions. The requisite advancements in technologies needed for future IoT infrastructures are also highlighted through a comparison of 5G and 6G capabilities. The results of the analysis indicate that 6G can contribute to a substantial improvement of IoT performance in smart cities, healthcare, industrial automation and control applications (manufacturing science), autonomous transportation and precision agriculture. However, issues concerning cybersecurity, interoperability, sustainability, spectrum management and infrastructure cost still remain. This study is expected to enable the development of secure, scalable, intelligent and sustainable next-generation IoT systems based on collaborative edge computing.
Implementation of support vector machine on LVMDP panel with overheating protection system Annas Singgih Setiyoko; Dimas Pristovani Riananda; Adianto Adianto
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1755-1766

Abstract

Electricity is a critical requirement in industrial operations, where the continuity and stability of power distribution directly affect safety and productivity. The low voltage main distribution panel (LVMDP) functions as the main node of electrical power distribution; however, conventional LVMDP systems generally lack intelligent protection mechanisms capable of detecting overheating-related fire hazards and initiating preventive action before failure occurs. This study proposes an intelligent monitoring and protection system for LVMDP panels that combines real-time multi-sensor monitoring, support vector machine (SVM)-based hazard classification, and an automatic shutdown mechanism. The main contribution of this work lies in the integration of predictive thermal risk detection with autonomous protective action, enabling the system not only to monitor panel conditions but also to respond immediately to hazardous states before they escalate into fire incidents. SVM was selected because of its strong capability to classify complex and nonlinear patterns from sensor data with high reliability. The developed system continuously evaluates panel conditions and triggers auto-shutdown when an overheating risk is identified, thereby improving preventive protection compared with conventional alarm-based monitoring systems. Experimental results show that the sensor measurements achieved error rates mostly below 5% compared with calibrated instruments, indicating good accuracy. In addition, the SVM model obtained an overall accuracy of 93%, with a macro-average F1-score of 92% and a weighted-average F1-score of 93%. These results demonstrate that the proposed system is effective for early detection and active protection of LVMDP panels against overheating hazards.

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