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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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Optimizing breast cancer diagnosis: combining hybrid architectures through Apache Spark Taib, Chaymae; Abdoun, Otman; Haimoudi, El Khatir
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.pp4261-4272

Abstract

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.
An improved modulation technique suitable for a three level flying capacitor multilevel inverter Mahafzah, Khaled A.; Negry, Raneem M.; Obeidat, Mohammad A.; Alsalem, Hesham
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.pp2522-2532

Abstract

This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed simplified modulation technique paves the way for more straightforward and efficient control of multilevel inverters, enabling their widespread adoption and integration into modern power electronic systems. Through the amalgamation of sinusoidal pulse width modulation (SPWM) with a high-frequency square wave pulse, this controlling technique attains energy equilibrium across the coupling capacitor. The modulation scheme incorporates a simplified switching pattern and a decreased count of voltage references, thereby simplifying the control algorithm.
Optimal control of the dynamics of nonlinear oscillating systems using synergetic principles of self-organization Xakimovich, Siddikov Isomiddin; Maxamadjanovna, Umurzakova Dilnoza
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.pp6271-6278

Abstract

This paper analyses the evolution of nonlinear oscillation control methods and presents an innovative approach known as analytical design of aggregated oscillation controllers (ADACO). This method is based on the principles of synergetic control theory and focuses on the integration of self-organization and control processes to synthesize energy-efficient control laws for nonlinear oscillating systems. The authors elaborate on the theoretical foundations of ADACO, which extends the previous analytical design of aggregated controllers (ADAC) method by incorporating energy invariants and integrals of motion into the synthesis of control laws. This approach demonstrates significant advantages over traditional methods, offering a versatile framework for the design of energy-efficient control systems for a wide range of nonlinear oscillating systems in various fields such as aerospace, robotics, vibromechanical systems, and objects with chaotic dynamics. The aim of the paper is to establish a unified approach to the control of nonlinear oscillations, solving both the problems of generation of stable oscillations and suppression of unwanted perturbations. The application of synergetic control principles in the framework of ADACO opens prospects for further development of nonlinear control theory.
Design of storage benchmark kit framework for supporting the file storage retrieval Naazre Vittal Rao, Sanjay Kumar; Munegowda, Keshava
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.pp1750-1758

Abstract

An open-source software framework called the storage benchmark kit (SBK) is used to store the system benchmarking performance framework. The SBK is designed to perform any storage client or device using any data type as a payload. SBK simultaneously helps number of readers as well as writes to the storage system of large amounts of data as well as allows end-to-end latency benchmarking for multiple writers and readers. The SBK uses standardized performance measures for comparing and evaluating various storage systems and their combinations. Distributed file systems, distributed database systems, single or local node databases, systems of object storage, platforms of distributed streaming and messaging, and systems of key-value storage are the storage solutions supported by SBK. The SBK supports various storage systems like XFS, Kafka streaming storage systems, and Hadoop distributed file system (HDFS) performance benchmarking. The experimental results show that a proposed method achieves execution time of 65.530 s, 40.826 s and 30.351 s for the 100k, 500k and 1000k files respectively which ensures better improvement than the existing methods such as simple data interface and distributed data protection system.
Blood glucose prediction using non-invasive optical system based on photoplethysmography Reguig, Mohammed Anes Bereksi; Labdelli, Nassima
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.pp5200-5208

Abstract

Several people must frequently evaluate their blood glucose since it is an important indicator of health problems mainly diabetes. Different medical systems are commercialized to measure blood glucose levels; some are invasive others are noninvasive. The main purpose of this article is to develop a non-invasive device for measuring blood glucose levels based on the detection and analysis of the photoplethysmogram signal. The developed systems include an optical sensor to detect the photoplethysmography (PPG) signal, digitalizing and acquiring boards to a computer and a software program to process and analyze the digitalized PPG signal regarding some features extracted from its waveform. These features are the systolic amplitude Sa and the b/a amplitude ratio in the second derivative PPG (SDPPG) waveform. An invasive glucometer is also used along with the Sa and b/a ratio determined from the developed system to generate a calibration model which is used to deduce blood glucose level (BGL) values. The result showed that the calibration model using the b/a ratio is more accurate for non-invasive blood level measurement then that of Sa with a difference in glucose estimation around 2 mg/dl and with the correlation coefficient (R2) of the glucose level prediction between 0.8904 and 0.9775.
Affective e-learning approaches, technology and implementation model: a systematic review Adebiyi, Marion Olubunmi; Adebiyi, Abayomi Aduragba; Olaniyan, Deborah; Orenyi, Bajeh Amos
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.pp631-644

Abstract

A systematic literature study including articles from 2016 to 2022 was done to evaluate the various approaches, technologies, and implementation models involved in measuring student engagement during learning. The review’s objective was to compile and analyze all studies that investigated how instructors can gauge students’ mental states while teaching and assess the most effective teaching methods. Additionally, it aims to extract and assess expanded methodologies from chosen research publications to offer suggestions and answers to researchers and practitioners. Planning, carrying out the analysis, and publishing the results have all received significant attention in the research approach. The study’s findings indicate that more needs to be done to evaluate student participation objectively and follow their development for improved academic performance. Physiological approaches should be given more support among the alternatives. While deep learning implementation models and contactless technology should interest more researchers. And, the recommender system should be integrated into e-learning system. Other approaches, technologies, and methodology articles, on the other hand, lacked authenticity in conveying student feeling.
Deep autoencoder based image enhancement approach with hybrid feature extraction for plant disease detection using supervised classification Huddar, Suma; Prabhushetty, Kopparagaon; Jakati, Jagadish; Havaldar, Raviraj; Sirdeshpande, Nandakishor
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.pp3971-3985

Abstract

Plant leaf diseases pose significant threats to global agriculture, leading to reduced crop yields and economic losses. Rapid and accurate disease detection is essential for timely interventions and sustainable farming practices. This study presents an innovative approach for plant leaf disease detection by integrating wavelet analysis, color, and texture features, coupled with autoencoder denoising and support vector machine (SVM) classification. Wavelet analysis is employed to extract multi-resolution features, capturing intricate details at different scales. Furthermore, color and texture characteristics are extracted to encompass a broad spectrum of visual information crucial for distinguishing diseases. The Autoencoder model helps to enhance the feature representation that mitigates the impact of noise and irrelevant data. The SVM classifier is utilized to learn complex patterns and accurately classify different disease classes. The combined model of wavelet, color, and texture attributes, in combination with autoencoder denoising and SVM classification, markedly enhances the precision and efficiency of disease detection in contrast to conventional methods. The system's performance is evaluated using a PlantVillage dataset, showcasing its adaptability to different plant species and disease types. The overall performance is obtained as 98.60%, 97.25%, 96.89%, and 97.20% in terms of accuracy, precision, recall, and F-Score, respectively.
Framework for smart contract blockchain in halal traceability, integrity, and transparency Munawar, Munawar; Mugiono, Arif
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.pp2875-2884

Abstract

Currently, the halalness of a product may only be determined by the halal certificate or halal label displayed on the product packaging. Simply put, several halal-related issues, like cross-contamination, logistical issues, halal counterfeiting, global halal acceptance, and so forth, keep coming up. To address the numerous issues raised above, it is believed that a framework that can support worldwide halal provisions is necessary. The objective of this research is to create a blockchain smart contract framework for halal transparency, integrity, and traceability. Blockchain technology along with smart contracts can solve classic tracing problems including data leakage, manipulation, and invisible data. Organizational consistency across the halal production process and the availability of raw materials that adhere to sharia laws are two more basic challenges with halal certification that blockchain and smart contracts can resolve. The usual traceability problems, such as data explosion, material cross-contamination between halal and haram, and revelation of private information, can all be resolved by smart contracts that make use of on-chain, off-chain, and electronic product code information services (EPCIS).
A novel scheme for unified streamlined traffic management in 5G backhaul network Thippanna, Deepa; Jayappa, Praveen
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.pp6981-6991

Abstract

The issues associated with the 5G backhaul network act as the underlying reason for concern. Existing literature towards the 5G backhaul network offers various ranges of sophisticated schemes that are yet to possess open-end issues. Therefore, the proposed study introduces a novel scheme for effective 5G backhaul network traffic management. The scheme hypothesizes that if an efficient gateway node is selected, it can better communicate between the macro-base station and the core network. The study contributes to developing a sophisticated system design to identify the blockage region, a macro-base station, and a small base station. These attributes incorporate the capability of a gateway node to identify the bottleneck condition during peak traffic situations in the 5G backhaul network. The study outcome shows better communication performance in contrast to the existing system. The study outcome shows the proposed scheme to offer 47% increased throughput, 80% reduced latency, and 55% reduced algorithmic processing time in contrast to existing schemes.
Efficient criticality oriented service brokering policy in cloud datacenters Subramanian, Shanmugapriya; Natarajan, Priya
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.pp2024-2034

Abstract

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.

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