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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 6,301 Documents
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Edge internet of things based smart home passwordless authentication Helal, Maha; Aldawsari, Abdullah; Al-Akhras, Mousa; Shawar, Bayan Abu; Omar, Hani
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7186-7197

Abstract

The internet of things (IoT) has transformed the way appliances and devices are connected and especially in the case of smart homes, in which smart devices can communicate through networks to improve everyday activities. However, it might be difficult to provide a high level of security for the data produced by these devices. Current security mechanisms might not always function adequately in all circumstances, especially when the number of devices increases. This research proposes an edge IoT-based smart home authentication scheme that adopts IPv6. For devices that use a smartphone application, it also offers a passwordless user authentication approach through the use of the smartphone ID and biometrics. The proposed authentication scheme was simulated to verify its ease of use and security. Security and cost analysis was also performed by reviewing and comparing the proposed scheme with previous research on IoT authentication systems. This research finds that the proposed authentication scheme is efficient at shielding home IoT networks from possible attacks, as well as maintaining a high level of usability.
Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization Hridoy, Rashidul Hasan; Arni, Arindra Dey; Ghosh, Shomitro Kumar; Chakraborty, Narayan Ranjan; Mahmud, Imran
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2181-2190

Abstract

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.
Deep learning model for diagnosing polycystic ovary syndrome using a comprehensive dataset from Kerala hospitals Rao, Divya; Dayma, Riddhi Rajendra; Pendekanti, Sanjeev Kushal; K., Aneesha Acharya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5715-5727

Abstract

Polycystic ovary syndrome (PCOS) requires early and precise diagnosis to manage and prevent long-term health consequences effectively. In this research, a large dataset of healthcare data gathered from various hospitals in Kerala, India, was evaluated using multiple machine learning (ML) and deep learning (DL) models to identify a highly reliable and accurate prediction of PCOS. The six algorithms used for comparison with the proposed DL model are support vector classification, random forest, logistic regression, k-nearest neighbors, and gaussian naive Bayes; they were selected due to their strengths in handling features in large datasets. The highly parameterized neural networks were tuned using efficient approaches like Optuna and genetic algorithms. The results indicated that the model implemented using our proposed combination of DL model and Optuna, outperformed the traditional models, achieving 93.55% reliability. This suggests the potential for using deep learning for decision-making in diagnosing PCOS. This method demonstrates the importance of integrating various data types with powerful analytic tools in medical diagnostics to support customized therapy.
An introduction to double stain normalization technique for brain tumour histopathological images Akmal Dzulkifli, Fahmi; Yusoff Mashor, Mohd; A. Raof, Rafikha Aliana; Jaafar, Hasnan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp375-388

Abstract

Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses.
Socio-technical factors influencing big data analytics adoption in healthcare Wolseley, Nik Nurdini; Salahuddin, Lizawati; Mohd Aboobaider, Burhanuddin; Raja Ikram, Raja Rina; Hashim, Ummi Rabaah; Abdul Rahim, Fiza
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4745-4758

Abstract

The purpose of this study is to determine the key socio-technical factors influencing big data analytics adoption in healthcare services. A systematic literature review was conducted using peer-reviewed scholarly publications spanning from 2013 to 2023 to illuminate the influencing factors. Twelve papers focused on the factors influencing big data analytics (BDA) adoption in healthcare services were included for review. The factors were divided into four major groups namely i) person, ii) technology, iii) organization, and iv) environment. Analytical skills define a person, whereas technology is characterized by system quality and information quality. Organization support, organization resources, training, data governance, and evidence-based decision-making are all associated with the organization. Finally, government regulations are allocated to the environment. This review presents evidence of the socio-technical factors that influence big data analytics adoption in healthcare services. The findings from this review recommend future big data analytics adoption in healthcare services to carefully evaluate the factors identified in this study.
A comparative analysis of convolutional neural networks for breast cancer prediction Al Tawil, Arar; Shaban, Amneh; Almazaydeh, Laiali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3406-3414

Abstract

Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.
Enhanced transformer long short-term memory framework for datastream prediction Dief, Nada Adel; Salem, Mofreh Mohamed; Rabie, Asmaa Hamdy; El-Desouky, Ali Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp830-840

Abstract

In machine learning, datastream prediction is a challenging issue, particularly when dealing with enormous amounts of continuous data. The dynamic nature of data makes it difficult for traditional models to handle and sustain real-time prediction accuracy. This research uses a multi-processor long short-term memory (MPLSTM) architecture to present a unique framework for datastream regression. By employing several central processing units (CPUs) to divide the datastream into multiple parallel chunks, the MPLSTM framework illustrates the intrinsic parallelism of long short-term memory (LSTM) networks. The MPLSTM framework ensures accurate predictions by skillfully learning and adapting to changing data distributions. Extensive experimental assessments on real-world datasets have demonstrated the clear superiority of the MPLSTM architecture over previous methods. This study uses the transformer, the most recent deep learning breakthrough technology, to demonstrate how well it can handle challenging tasks and emphasizes its critical role as a cutting-edge approach to raising the bar for machine learning.
Solar-based aerator with water quality monitoring in vannamei shrimp pond Pratama, I Putu Eka Widya; Kusuma, Friska Aprilia; Mujiyanti, Safira Firdaus; Schirhagl, Romana; Nanta, Tepy Lindia
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5048-5054

Abstract

The water quality is vital for the vannamei shrimp pond's productivity. Manual monitoring at Gunung Anyar's vannamei shrimp pond is time-consuming, ineffective, and potentially harmful. In this research, we developed a real-time monitoring system for the water quality of the vannamei shrimp pond. This monitoring system is integrated with a solar-based aerator. To address this, water quality monitoring in a solar-based aerator system tracks the degree of acidity (pH), temperature, and total dissolved solids (TDS) remotely using a website and real-time mobile phone Android application with 98.57% accuracy and 1.43% error. Seven days of data revealed the degree of acidity between 6.92 and 7.34 is indicated poor conditions of the pond While the temperatures from 23.59 °C to 38.32 °C, and TDS from 628.65 to 652.34 ppm indicate the good condition of the shrimp pond. This real-time monitoring system can help vannamei shrimp farmers monitor the actual conditions of their ponds.
An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips Goravi Sukumar, Pavithra; Krishnaiah, Modugu; Velluri, Rekha; Satish, Pooja; Nagaraju, Sharmila; Gowda Puttaswamy, Nandini; Srikantaswamy, Mallikarjunaswamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp305-314

Abstract

The deadlock-free and live lock-free routing at the same time is minimized in the network on chip (NoC) using the proposed adoptive reconfigurable routing protocol (ARRP). Congestion condition emergencies are avoided using the proposed algorithm. The input packet distribution process is improved among all its shortest paths of output points. The performance analysis has been initiated by considering different configuration (N*N) mesh networks, by sending various ranges of data packets to the network on chip. The average and maximum power dissipation of XY, odd-even, Dy-XY algorithm, and proposed algorithm are determined. In this paper, an analysis of gate utilization during data packet transfer in various mesh configurations is carried out. The number of cycles required for each message injection in different mesh configurations is analyzed. The proposed routing algorithm is implemented and compared with conventional algorithms. The simulation has been carried out using reconfigurable two-dimensional mesh for the NoC. The proposed algorithm has been implemented considering array size, the routing operating frequency, link width length, value of probability, and traffic types. The proposed ARRP algorithm reduces the average latency, avoids routing congestion, and is more feasible for NoC compared to conventional methods.
A fast charge algorithm for Li-ion battery for electric vehicles Bouzaid, Sohaib; El Mehdi, Laadissi; El Ballouti, Abdessamad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2457-2465

Abstract

The renewable solar energy industry and electric vehicle industry are today seeking for fast battery pack recharging methods to achieve higher performances, and fast energy recovery for energy storage systems (ESS) and for electric vehicles. The charge rate of batteries impacts directly the temperature which in turn impacts the capacity fade, thus it should be kept low to prevent the cells from warming up. This not only limits the charging rate but also puts us on a trade-off, a long lifetime or a fast recharge. In this study, we tried to achieve fast charging using a new charging method that combine two charging methods, without much deterring the capacity of the battery, in order to be able to maintain a long battery lifetime. Charging time of around 82 min was achieved for a 1.8 Ah battery. We compared our findings with the literature with known charging profiles.

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