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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 2,901 Documents
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Skin cancer diagnosis using the deep learning advancements: a technical review Pandey, Shailja; Shankhdhar, Gaurav Kant
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5925

Abstract

It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation.
A comparative analysis of activation functions in neural networks: unveiling categories Bouraya, Sara; Belangour, Abdessamad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7274

Abstract

Activation functions (AFs) play a critical role in artificial neural networks, allowing for the modeling of complex, non-linear relationships in data. In this review paper, we provide an overview of the most commonly used AFs in deep learning. In this comparative study, we survey and compare the different AFs in deep learning and artificial neural networks. Our aim is to provide insights into the strengths and weaknesses of each AF and to provide guidance on the appropriate selection of AFs for different types of problems. We evaluate the most commonly used AFs, including sigmoid, tanh, rectified linear units (ReLUs) and its variants, exponential linear unit (ELU), and SoftMax. For each activation category, we discuss its properties, mathematical formulation (MF), and the benefits and drawbacks in terms of its ability to model complex, non-linear relationships in data. In conclusion, this comparative study provides a comprehensive overview of the properties and performance of different AFs, and serves as a valuable resource for researchers and practitioners in deep learning and artificial neural networks.
Improving sentiment analysis using text network features within different machine learning algorithms Alnasrawi, Ali Mohamed; Alzubaidi, Asia Mahdi Naser; Al-Moadhen, Ahmed Abdulhadi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5576

Abstract

Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.
Sentiment analysis with hotel customer reviews using FNet Bhowmik, Shovan; Sadik, Rifat; Akanda, Wahiduzzaman; Pavel, Juboraj Roy
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6301

Abstract

Recent research has focused on opinion mining from public sentiments using natural language processing (NLP) and machine learning (ML) techniques. Transformer-based models, such as bidirectional encoder representations from transformers (BERT), excel in extracting semantic information but are resourceintensive. Google’s new research, mixing tokens with fourier transform, also known as FNet, replaced BERT’s attention mechanism with a non-parameterized fourier transform, aiming to reduce training time without compromising performance. This study fine-tuned the FNet model with a publicly available Kaggle hotel review dataset and investigated the performance of this dataset in both FNet and BERT architectures along with conventional machine learning models such as long short-term memory (LSTM) and support vector machine (SVM). Results revealed that FNet significantly reduces the training time by almost 20% and memory utilization by nearly 60% compared to BERT. The highest test accuracy observed in this experiment by FNet was 80.27% which is nearly 97.85% of BERT’s performance with identical parameters.
Swin transformer adaptation into YOLOv7 for road damage detection Irsal, Riyandi Banovbi Putera; Utaminingrum, Fitri; Ogata, Kohichi
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7556

Abstract

Highways are an important component of any country. However, some highways in Indonesia endanger users while maintaining road safety. Crack detection early in the deterioration process can prevent further damage and lower maintenance costs. A recent study sought to develop a method for detecting road damage by combining the road damage detection (RDD) dataset with generative adversarial network technology and data augmentation to improve training. The current study aims to broaden the you only look once (YOLO) framework by incorporating the Swin Transformer into the chiral stationary phases (CSP) component of YOLOv7, with the goal of improving object detection accuracy in a variety of visual scenarios. The study compares the performance of various object detection models with varying parameters and configurations, such as YOLOv5l, YOLOv6l, YOLOv7-tiny, YOLOv7, and YOLOv7x. YOLOv5l has 46 million parameters and 108 billion floating point operations per second (FLOPS), whereas YOLOv6l has 59.5 million parameters and 150 billion FLOPS. With 31 million parameters and 140 billion FLOPS, the YOLOv7-swin model performs best with mean average precision (mAP), mAP_0.50 of 0.47. and mAP_0.5:0.95 of 0.232. The experimental results show that our YOLOv7-swin model outperforms both YOLOv7x and YOLOv7-tiny. The proposed model significantly improves object detection accuracy while keeping complexity and performance in balance.
Potential and economic feasibility analysis of solar-biomass-based hybrid system for rural electrification Channi, Harpreet Kaur; Giri, Nimay Chandra; Sandhu, Ramandeep; I. Abu El-Sebah, Mohamed; Syam, Fathy Abdelaziz
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7760

Abstract

A significant portion of the population lives in rural regions where the grid cannot provide them with enough power. Rising power demand, fossil fuel prices, limited fossil fuels such as coal, and environmental issues are the key drivers driving the usage of renewable energy resources for generating electricity. As a result, an alternate option for electricity generation in such remote places is required. Using renewable resources as alternatives would undoubtedly aid in mitigating the effects of global warming. The hybrid energy system combines electric power production with renewable sources such as solar, biomass, wind, biogas, hydro, and diesel generators (DGs). In light of this, a feasibility study on hybrid renewable energy was carried out for a specified remote region. This research investigates the efficacy of a solar-biomass-based hybrid power generation for rural electrification. The effective and sustainable alternative is found in a standalone hybrid version based on solar biomass. Electricity produced from the hybrid model proposed is $0.603.555 per unit, which is almost free of emissions of greenhouse gas (GHG), equally economical, and cleaner than the traditional supply. This system can be beneficial to electrify other adjacent remote zones.
The model of decision support system using hybrid method and actual weighting for the study program ranking Amin, M. Miftakul; Dwitayanti, Yevi
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.7038

Abstract

Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.
Shielding privacy: a technique of extenuating composition attacks in various independent data publication Faruq, Md. Omar; Walid, Md. Abul Ala; Baowaly, Mrinal Kanti; Devnath, Maloy Kumar; Ejaz, Md. Sabbir; Barman, Pronob Kumar; Sattar, A H M Sarowar
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6763

Abstract

Protecting personal information from unauthorized access is a critical concern for individuals. However, the accumulation of confidential information by various organizations, such as banks and hospitals, for regular communication creates a potential vulnerability. If an individual visits two hospitals and both facilities independently release the individual's gathered data, a malicious adversary could potentially deduce confidential information through a composition attack. Therefore, developing methods that protect individuals from composition attacks is crucial. According to the size of the dataset and the percentage of overlapping persons, our study examines the effectiveness of composition attacks. We propose a knowledge domain-based design to mitigate successful composition attacks, which has shown promising results in reducing such attacks and compared to existing studies based on the k-anonymity and l-diversity models. Our approach leverages a knowledge domain to reduce the likelihood of data breaches, demonstrating the effectiveness of our method in protecting individuals' privacy and preventing unauthorized access to sensitive information. Finally, the effects of data utility on the diverse data set have been measured.
Covid-19 forecasting model based on machine learning approaches: a review Sayeed, Md Shohel; Hishamuddin, Siti Najihah; Song, Ong Thian
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7304

Abstract

As coronavirus disease (Covid-19) it is a contagious disease that is spread by the SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was initially discovered in Wuhan, China, in 2019, and has now spread throughout the world, including Malaysia. A large number of people have lost their life partners and families because of this disease. Thus, in order for us to stop this epidemic spread, we have to implement social distance. The Covid-19 infection displays this type of behavior, which necessitates the development of mathematical and predictive modeling techniques capable of predicting possible disease patterns or trends, in order to assist the government and health authorities in predicting and preparing for potential outbreaks. The purpose of this paper is to provide an in-depth critique and analysis of the machine-learning approaches that have been implemented by researchers to predict Covid-19, based on existing research. As a result, future researchers will be able to use this paper as a valuable resource for their research related to the Covid-19 forecasting model.
Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques Hamida, Soufiane; Lamrani, Driss; El Gannour, Oussama; Saleh, Shawki; Cherradi, Bouchaib
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6313

Abstract

Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.

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