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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 3,126 Documents
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Improving delivery mode forecasting with deep neural network: a time-based convolutional network strategy Saeed Hamouda; Ayman Mohamed; Hany A. Elsalamony
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10868

Abstract

The caesarean section is one of the most frequently performed surgical procedures worldwide, with profound implications for maternal and neonatal health. Accurate prediction of delivery mode is essential for guiding clinical decisions, minimizing unnecessary surgical interventions, and improving patient outcomes. This study introduces a deep neural learning technique based on a temporal convolutional neural network (DNLTC) to classify delivery type—caesarean section versus normal vaginal delivery using maternal and obstetric data. The proposed model was evaluated against traditional machine learning (ML) approaches, including artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT). Experimental results show that the DNLTC achieved the highest overall accuracy (85%), surpassing ANN (80%), SVM (68.8%), and DT (65%). TCNN also demonstrated strong clinical reliability, with a sensitivity of 94%, specificity of 91%, and a perfect F1-score of 100%. These findings highlight the advantages of incorporating temporal feature learning into delivery mode prediction, enabling the detection of subtle, sequential patterns that conventional models may overlook. By providing more accurate and robust predictions, the proposed framework can support obstetricians in making timely, evidence-based decisions, ultimately enhancing maternal and newborn health outcomes.
Papulosquamous SkinSense: a hybrid artificial intelligence model with visual explanations and chatbot Krupali Rupesh Dhawale; Arvind R. Bhagat Patil
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10656

Abstract

Accurate diagnosis of papulosquamous skin diseases such as Psoriasis (PS), Lichen Planus (LP), Pityriasis Rosea (PR), and Pityriasis Rubra Pilaris(PRP) is challenging due to their visually similar features, particularly during healing stages. This study presents an optimized deep learning framework to improve diagnostic accuracy and interpretability. A dataset of 3,120 DermsNetZ images was used, with preprocessing through contrast limited adaptive histogram equalization(CLAHE) to enhance lesion visibility. Pretrained convolutional neural networks (CNNs) including MobileNetV2, InceptionV3, NASNet, and hybrid models were evaluated using accuracy, precision, recall, and F1-score. Among these, MobileNetV2 combined with gradient-class activation mapping (Grad-CAM) achieved the best results, delivering 94% accuracy, 95% precision, and strong F1-scores, while offering explainable artificial intelligence (AI) through lesion localization. To translate these results into practice, the SkinSense Detection App was developed, integrating with transfer learning, class balancing, augmentation, and Grad-CAM visualization within a user-friendly interface. The app also incorporates a large language model (LLM-powered) chatbot for real-time, personalized feedback. With an overall success rate of 98.08% and user ratings between 4.6–4.8/5, the system demonstrates high reliability and accessibility. This study highlights the value of interpretable deep learning in dermatology, bridging technical accuracy with clinical usability and offering scope for expansion to larger datasets and diverse skin conditions.
Intelligent innovation across disciplines: key trends from recent research in AI, IoT, and automation Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.4857

Abstract

The fast growth of artificial intelligence (AI), the Internet of Things (IoT), and automation is changing research and business in many fields. This issue has 76 new pieces of research that show new ways to use technology and new ways to accomplish things in healthcare, communication networks, smart energy, farming, and industrial automation. Deep learning and hybrid AI models improve disease identification, medical picture segmentation, and patient monitoring in healthcare, focusing on both accuracy and ease of understanding. In communication and the IoT, contributions include smart resource allocation for 5G and 6G networks, spectrum sensing, and cybersecurity frameworks that deal with efficiency, latency, and resilience in connected environments. AI and the IoT are important for sustainability and city planning. For example, smart energy management, predicting the weather in greenhouses, and using drones to monitor traffic are all examples of this. Also, the development of autonomous guided cars and Industry 5.0 automation shows how intelligent systems can be used in industrial operations. These works show how AI, IoT, and automation are coming together and how they could change the way people operate, make decisions, and solve problems in many fields. This issue shows how important it is to use ideas from many fields to drive the next generation of smart innovation
Resource cost management in cloud service environment Kong FanYong; Fang-Fang Chua; Amy Hui-Lan Lim
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10352

Abstract

Cloud computing has revolutionized information technology (IT) infrastructure by enabling on-demand access to scalable resources. However, the elasticity and complexity of cloud billing models introduce significant challenges for effective resource cost management. This paper proposes a hybrid framework integrating statistical models auto regressive integrated moving average (ARIMA), machine learning techniques long short-term memory (LSTM), and optimization methods deep deterministic policy gradient (DDPG) to forecast and manage cloud costs with enhanced accuracy and adaptability. The framework is empirically validated using synthetic billing datasets and real-world cloud provider data, with performance evaluated via root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics. Results demonstrate 15-25% improvement in cost prediction accuracy over baseline models and up to 20% cost savings through dynamic resource allocation. The framework extends beyond traditional VM-based workloads to support serverless computing (amazon web services (AWS) Lambda and Azure Functions) and container-based applications (Docker and Kubernetes), addressing the growing adoption of microservices architectures. Comparative analysis with existing tools (AWS Cost Explorer and Azure Advisor) reveals superior adaptability in multi-cloud environments. The paper concludes with discussions of emerging paradigms including FinOps practices, AIOps automation, and sustainability-aware resource allocation, outlining future research directions toward explainable AI-driven cost governance.
Dynamic weight adaptation in soft voting for emotion detection using neural networks Nisha Nisha; Rakesh Kumar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10628

Abstract

Confirming elevated accuracy and speed in multi-label automatic emotion classification endures to pose extensive challenges. Old-style machine learning (ML) models are broadly used for this. However, large-scale fast embryonic textual information often obstructs their performance. Deep learning (DL) models resolve the former problem efficiently, but fine-tuning the hyperparameter entails a lot of work and experience. Ensemble learning practices offer enhanced accuracy, but classical soft voting classifiers with static weights fall short to adapt effectively to diverse data traits. To tackle this limitation, this study proposes a novel ensemble framework that employs a neural network (NN) based dynamic weight adaptation within a soft voting classifier. The model dynamically adjusts the weights of core ML classifiers based on their real-time predictive likelihood and performance statistics. This adaptive weighting suggestively enhances the model’s ability in detecting nuanced emotional expressions in text, improving responsiveness and generalization. Comprehensive experiments conducted on yardstick emotion dataset demonstrate that proposed integration of NN driven adaptive weighting within an ensemble framework outpaces traditional approaches, capturing an overall classification accuracy of approximately 98% thus offering a scalable and robust solution for real-world sentiment analysis applications.
Harmonic path planning using Quarter-Sweep Boosted AOR iterative method Sumiati Suparmin; Azali Saudi
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.7286

Abstract

This paper presents the findings of a study that examined the effectiveness of the application of Quarter-Sweep Boosted AOR with the 9-Point Laplacian operator using the families of relaxation methods for computing the solutions of Laplace's equation to obtain the harmonic potentials+ This work is a continuation from the past study that applied the standard 5-Point Laplacian to solve path planning issue that a mobile robot faces when working in indoor environment. The robot can navigate from a given starting position to a goal position by following the safest path, ensuring it avoids any obstacles and minimizes the risk of collisions. By utilizing Laplace's equation and computing the distribution of potential values in the simulated environments, the robot can determine the safest path that avoids obstacles present in the environment. This method ensures that the robot moves along a path where the potential for collisions is minimized. The findings confirm that Quarter-Sweep Boosted AOR (QSBAOR) outperforms Half-Sweep Boosted AOR (HSBAOR) and Full-Sweep Boosted AOR (FSBAOR). QSBAOR and HSBAOR show 75% and 50% reduction respectively, compared to FSBAOR in terms of computational complexity.

Page 313 of 313 | Total Record : 3126

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