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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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Surveillance detection of anomalous activities with optimized deep learning technique in crowded scenes Omobayo Ayokunle Esan; Dorcas Oladayo Esan; Munienge Mbodila; Femi Abiodun Elegbeleye; Kesewaa Koranteng
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4471

Abstract

The performance of conventional surveillance systems is challenged by high error detection rates in busy scenes, which has significantly affected the accurate detection of the current surveillance system. Feature representation and object pattern extraction from different scenes have made deep learning (DL) promising methods in surveillance systems, compared to the approaches where features are created manually. To improve the detection accuracy, this paper presents an intelligent DL technique that combines convolutional neural network (CNN) and long short-term memory (LSTM). CNN extracts and learns the object features from a set of raw images, while the LSTM is then used by gated mechanisms to store important information from the extracted features. The proposed method was validated using datasets from the University of California San Diego (UCSD). The result shows that the model achieves 95% accuracy, which is superior compared to other conventional detection models.
Mobile game model for monitoring Malaysian food calories intake using image recognition Nurmaisarah Ismail; Sazilah Salam; Siti Nurul Mahfuzah Mohamad; Bambang Pudjoatmodjo; Norazlina Shafie; Rashidah Lip; Mohd Adili Norasikin; Faaizah Shahbodin
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4916

Abstract

Two important problems related to food consumption were reported in Malaysia: Malaysia was the sixth rank in Asia for the highest adult obesity rate; and the United Nation reported that Malaysian consumed an average of 2,910 calories per day. An imbalanced diet and high intake of calorie-dense food problems that need attention to reduce obesity. These problems affect national economies by lowering productivity, increasing disability, raising health care expenses, and shortening life spans. Although, there are food calorie tracking applications available, however, existing apps are less engaging and to recognize Malaysian food due to its not versatile databases. This can be solved using game technologies. Hence, this study will propose mobile game model as a solution to the underlying problems. There are 4 phases in the method: expert validation, initial model, expert verification, and final model. The proposed parameters were validated by dietitians, and nutritionists. The model was verified by game experts. A low fidelity prototype was developed based on the proposed model to assist the expert verification process. The model was finalized based on the expert’s feedback. The proposed game model resolves the limited recognition of Malaysian food and monitoring the food calories intake in an engaging way.
Prospects for developing digital telecommunication complexes for storing and analyzing media data Vladimir Kuklin; Islam Alexandrov; Dmitry Polezhaev; Aslan Tatarkanov
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4840

Abstract

In today's digital world, saturated with data flows, universal multifunctional systems are developing, capable of solving various problems related to optimizing the use of available computing resources. A distinctive feature of such systems is the heterogeneity of incoming flows of user requests due to the multifunctionality of modern information systems, expressed in supporting various multimedia services on a single platform. Data heterogeneity and large volumes of data create many problems related to the speed of digital systems and data storage security. The solutions can be found in artificial intelligence (AI) technologies, particularly machine learning. Therefore, development and implementation of digital telecommunication complexes for storing, processing, and forming a dynamic flow of multiformat data using AI technologies are becoming more relevant. This paper aims to identify trends and prospects for developing these complexes, and develop proposals on their perspective characteristics. The authors focused on review the experience of Russian organizations developing multi-object analytics systems and analyze the technical and functional characteristics of existing systems. The result of the review and analysis is a table with a comparison of the technical characteristics of existing complexes and proposals for characteristics that are promising for further implementation.
Development of playfair cryptosystem based on generation a multi-dimensional key matrix Mustafa Dhiaa Al-Hassani; Methaq Talib Gaata
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5052

Abstract

Playfair is considered as one of the classical encryption symmetric methods, it has a limitation of using just 5×5 matrix, which means only 25 English letters could be represented. In this work, a 2D and 3D method is adopted as an expanded matrix that encompass the overall American standard code for information interchange (ASCII) codes in a permuted manner for all symbols of any language. Any sort of the multi-dimensional matrix will enhance the security by increasing the complexity on the attacker to try 256! patterns of keys probabilities instead of 25!. The key-matrix is generated from the chaotic maps for some control parameters as patterns of non-repeating random numbers from 0 to 255 equivalent to their ASCII code values. The security of the proposed method not rely only on the number of key probabilities, but exceed that to: matrix dimensionality, encryption/decryption algorithms, initial chaotic parameters, and key-matrix values permutation. The efficiency of the proposed cryptosystem has been investigated when tested on 784 samples according to security measurements in which the obtained number of pixels change rate (NPCR) (99.609) is very close to the ideal value, while the correlation plotting close to zero (0.00058) and entropy near from 8 (7.9998).
Pulse charging based intelligent battery management system for electric vehicle Sunil Somnath Kadlag; Pawan Tapre; Rahul Mapari; Mohan Thakre; Deepak Kadam; Dipak Dahigaonkar
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4564

Abstract

Electric vehicles (EVs) are now an important part of the automotive industry for two main reasons: decreased reliance on oil and reduced air pollution, which helps us contribute to the development of an environmentally friendly environment. EV buyers examine overall vehicle mileage, recharge time, vehicle mileage after every charge, batteries charging/discharging security, lifespan, charged rate, capability, and temperature increase. A new improved pulse charging technique is proposed, in which the battery is charged using proportional integral derivative (PID) control action and a neural network. A PID controller is used to develop the charging unit in this design. The feed forward neural network was used to determine the values of the PID control parameters. The battery management system (BMS) ensures that this designed battery charging system takes less time to charge the battery efficiently. The system is built with MATLAB/Simulink.
Glaucoma classification using a polynomial-driven deep learning approach Krishna Santosh Naidana; Soubhagya Sankar Barpanda
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4670

Abstract

In this paper, a deep learning-based multi-stage polynomial driven glaucoma classification-net (PDGC-Net) has been proposed for glaucoma identification through retinal images. The proposed approach begins with retinal image pu[1]rification by noise estimation and reduction. Noise has been estimated using a polynomial coefficient-based approach. Images are classified using PDGC-Net, whose polynomial indeterminate representative blocks are designed using new convolutional neural networks (CNN) architectures. The performance of PDGC[1]Net has been observed on the ACRIMA, ORIGA, and retinal image database for optic nerve evaluation (RIM-ONE) datasets. The experimentation is carried out on noisy and denoised images separately, and PDGC-Net has achieved 96% to 98% and 98% to 100% accuracy ranges, respectively. The model’s elasticity is tested with various stages of PDGC-Net. The quantitative PDGC-Net perfor[1]mance analysis is done with state-of-the-art CNN models. The proposed model’s performance has been proven and could be an effective aid to ophthalmologists for glaucoma screening (GS).
Enhanced and authenticated cipher block chaining mode Yasmeen Shaher Alslman; Ashraf Ahmad; Yousef AbuHour
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5113

Abstract

Due to the increased attacks on different applications, data security has become crucial. Many modes can be used to operate the advanced encryption standard (AES), some of which provide integrity, and some outperform other modes in security and simplicity. In this paper, the chain block cipher (CBC) mode has been modified to provide more security to the encrypted data by making it robust against the bit-flipping attack and adding an integrity approach using the keyedhash function. In addition, using the keyd-hash function increases the number of keys needed in CBC-AES to two keys, and this can make the proposed model more secure against bruteforce attacks and Grover’s quantum search algorithm.
Using machine learning approach towards successful crowdfunding prediction Sarifah Putri Raflesia; Dinda Lestarini; Rizka Dhini Kurnia; Dinna Yunika Hardiyanti
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5238

Abstract

Crowdfunding is a concept that emerged due to difficulties in raising funds for community business projects, social activities, micro-enterprises, and start-ups conventionally. Crowdfunding uses internet technology as a bridge between the donor and the recipient of funds so that it can reach a wider range of donors. This study aims to compare the performance of machine learning approaches in predicting crowdfunding campaign success. Three machine learning algorithms were employed to predict crowdfunding campaign success, namely logistic regression, random forest, and extreme gradient boosting (XGBoost). The dataset used in this study contains data about all projects posted on Kickstarter from January 2020 to September 2022. To improve the prediction model's performance, experiments using principal component analysis (PCA) feature reduction and log transformation were conducted. The results show that the implementation of log transformation on the dataset can increase the prediction model's performance. Meanwhile, XGBoost algorithm performs better than linear regression and random forest.
SrAlOCl:Bi3+@SiO2 phosphor with broad emission band and its effects on LED optical power and correlated color temperature Ha Thanh Tung; Huu Phuc Dang; Hoang Thinh Nhan
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4948

Abstract

Sr3Al2O5Cl2:Bi3+ (SAlOCl:Bi3+)phosphor for broadband emission was made using a solid-state method. From the extensive spectroscopic analysis and theoretical computation, significant conclusions about the origin of the Bi3+ emission were drawn. For the Sr 3 and Sr 1 sites, respectively, the dipole-quadrupole and quadrupole-quadrupole interactions were responsible for the concentration quenching in SAlOCl:Bi3+. The resulting luminescence mechanism demonstrated that the crystallization of Bi3+ at the two sites is what causes the emission from each site. The warm white light emitting diodes (LED) models were built with a 380-nm ultraviolet (UV) chip, SAlOCl:Bi3+, and two other phosphors. Then, the color rendering indeces (CRI) and the correlated color temperature (CCT) were calculated. Particularly, the CRI values ranged from 84.3 to 86.2 under operating currents of 20–50 mA, respectively. The increasing SAlOCl:Bi3+ dosage also heightened particle density, resulting in higher scattering coefficients. High scattering results in improved color coordination (lower color variance). The CRI and luminous flux are reduced as the phosphor SAlOCl:Bi3+ concentration increases more than owing to color loss and energy loss by backscattering and re-absorption. Thus, it is advisable to consider SAlOCl:Bi3+ carefully before applying in production.
Features selection for estimating hand gestures based on electromyography signals Raghad R. Essa; Hanadi Abbas Jaber; Abbas A. Jasim
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5048

Abstract

Hand prosthesis controlled by surface electromyography (sEMG) is promising due to the control capabilities and the noninvasive technique that machine learning (ML) offers to help physically disabled people during daily life. Nevertheless, dexterous prostheses are still infrequently popular due to control problems and limited robustness. This paper proposes a new set of time domain (TD) features to improve the EMG pattern recognition performance. The effect of five feature sets is evaluated based on the three classifiers k-nearest neighbor (KNN), linear discriminate analysis (LDA), and support vector machine (SVM). The EMG signals are obtained from database-5 (DB5) of the ninapro project datasets. In this study, the long-term signals of DB5 are segmented into short-term signals to perform short-term recognition. The results showed that the LDA classifier based on the proposed features achieved high classification accuracy for classifing 17 gestures. The LDA classifier achieved about 96.47% compared to 94.12%, and 93.82% for KNN and SVM classifiers, respectively. The results confirm that the suitable features extracted from short term signals with the appropriate classifier, has an important impact on improving the performance of gesture classification.

Page 181 of 291 | Total Record : 2901

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