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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 111 Documents
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Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
Smart fuzzy incubator for free-range chicken on internet of things Islamiyah, Mufidatul; Arifin, Samsul
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.pp5344-5355

Abstract

This paper presents the design of an artificial incubator for free-range chicken eggs. The incubator is designed to maintain optimal conditions and humidity for embryo development and this incubator is equipped with an egg turning mechanism. This incubator is designed using NodeMCU 8266 hardware which is used as a control that connects to the database server, and this incubator is built using multiplex, exhaust fan, mist maker, motor tuning, DS18B20, DHT-11 and real-time clock (RTC) DS3231. The temperature and humidity conditions in the incubator are maintained within the desired range and the egg changing mechanism works effectively. The results of this research show that this artificial incubator can be a reliable and effective tool for hatching free-range chicken eggs. This incubator is very easy to use and maintain and very affordable for local free-range chicken farming. This future research will focus on determining the conditions under which the incubator will provide the best results in terms of hatching efficiency for free-range chickens.
Integrating numerical methods and machine learning to optimize agricultural land use Tynykulova, Assemgul; Mukhanova, Ayagoz; Mukhomedyarova, Ainagul; Alimova, Zhanar; Tasbolatuly, Nurbolat; Smailova, Ulmeken; Kaldarova, Mira; Tynykulov, Marat
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.pp5420-5429

Abstract

In the current context, optimizing the utilization of agricultural land resources is increasingly vital for production intensification. This study presents a methodological approach employing numerical methods and machine learning algorithms to analyze and forecast land use optimality. The objective is to develop effective models and tools facilitating rational and sustainable agricultural land resource management, ultimately enhancing productivity and economic efficiency. The research employs data dimensionality reduction techniques such as principal component analysis and factor analysis (FA) to extract key factors from multidimensional land data. The simplex method is utilized to optimize resource allocation among crops while considering constraints. Machine learning algorithms including extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM) are employed to predict optimal land use and yield with high accuracy and efficiency. Analysis reveals significant differences in model performance, with LightGBM achieving the highest accuracy of 99.98%, followed by XGBoost at 95.99%, and SVM at 43.65%. These findings underscore the importance of selecting appropriate algorithms for agronomic data tasks. The study's outcomes offer valuable insights for formulating agricultural practice recommendations and land management strategies, integrable into decision support systems for the agricultural sector, thereby enhancing productivity and production efficiency.
Developing an effective focused crawler to retrieve data of Indian-origin scientists and utilizing text classification for comparative analysis Gautam, Shivani; Bhatia, Rajesh; Jain, Shaily
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.pp5468-5480

Abstract

This article presents the implementation of focused web crawling to retrieve data about scientists of Indian ancestry who are working in foreign nations. This study demonstrates the effectiveness of web scraping in obtaining large amounts of data from publicly available online pages. The objective is to construct a collection of data pertaining to Indian scientists who are now employed in national laboratories overseas. Collecting a vast quantity of data on the aforementioned Indian scientists through manual search is a pointless task. Therefore, this study proposes a detailed plan for a focused web crawler that can gather similar data. Subsequently, we present a comprehensive assessment of numerous classification models on this newly created dataset. Our assessments indicate that the random forest model surpasses the other supervised models. The empirical findings on large datasets demonstrated that the combination of random forest with synthetic minority oversampling technique (SMOTE) and k-fold cross-validation methods yielded better performance compared to K-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR) for Indian origin scientists. Conversely, SMOTE with an 80-20 random split demonstrated superior performance on smaller datasets. Overall, the random forest classifier demonstrated the most favorable outcomes, attaining a micro-average area under curve (AUC) of 90%. The outcomes of our study provide a solid foundation for further investigation into classification of text of Indian origin scientists.
Performance evaluation of machine learning algorithms for meat freshness assessment Arsalane, Assia; Klilou, Abdessamad; Barbri, Noureddine El
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.pp5858-5865

Abstract

In meat industry, a non-destructive evaluation and prediction of meat quality attributes is highly required. Artificial vision technology is a powerful and widely used tool for meat quality evaluation because of reliability, reproducibility, non-invasiveness, and non-destructiveness. Machine learning methods are a fundamental and crucial part of artificial vision technology. Their choice is critical in determining successfully the quality of meat. The goal of this paper was to compare the performance of three artificial intelligence-based methods to evaluate the beef meat freshness. In this research, a dataset of beef meat samples images was used to extract the color and texture features. Different methods including the support vector machines (SVM), k-nearest neighbor (KNN), and naïve Bayes (NB) algorithms were applied to determine the freshness of samples. The accuracy rates of KNN, SVM and NB algorithms were obtained about 92.59%, 90.12% and 87.65%, respectively. The results show that the KNN provides the highest classification rates against SVM and NB algorithms.
Deep reinforcement learning based quality of experience aware for multimedia video streaming Reddy, Manjunatha Peddareddygari Bayya; Narayanappa, Sheshappa Shagathur
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.pp5209-5220

Abstract

Video streaming involves the continuous delivery of video files from a server to a client, where multimedia streaming is employed for playback through an online or offline media player. Video streaming uses live broadcasts to enhance direct communication with community partners and customers. The existing methods have less video streaming quality and are unable to efficiently adapt to the dynamic conditions of the network. In this research, an adaptive bit rate (ABR) method depending on dynamic adaptive video streaming over hypertext transfer protocol or HTTP (DASH) based deep reinforcement learning (DRL) named DASH-based DRL is proposed to determine the following segment’s quality in DASH video streaming with wireless networks. The proposed algorithm significantly improves the quality of experience (QoE) performance by providing a highly stable video quality, reducing the distance factor, and enduring smooth streaming sessions. The performance of the proposed method is analyzed based on performance measures of performance improvement, QoE metrics, mean opinion score, normalized value of QoE, average of normalized value of QoE, switching quality, and rebuffering time. The suggested algorithm obtains a high average normalized QoE of 0.72, average switching quality of 0.15, and an average rebuffering time of 0.16 sec, which is comparatively superior to other algorithms like real-time streaming protocol (RTSP), HTTP live streaming (HLS) and reinforcement learning (RL).
Novel cryptosystem integrating the Vigenere cipher and one Feistel round for color image encryption Tabti, Hassan; El Bourakkadi, Hamid; Chemlal, Abdelhakim; Jarjar, Abdellatif; Najah, Said; Zenkouar, Khalid
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.pp5701-5714

Abstract

The present research will propose an innovative technique for pixel-level encryption of color images. After isolating the R, G, and B channels and converting them into vector mode, an enhanced Feistel network will be applied at the hexadecimal level, facilitated by integrating a substitution table generated from the employed chaotic maps. This is followed by a binary conversion and a shift ensured by pseudo-random vectors. A diffusion function is applied, incorporating another replacement matrix constructed from commonly used chaotic maps in cryptography. This operation links the cipher pixel to the next pixel, thereby reinforcing the avalanche effect and safeguarding the system against any differential attacks. Simulations conducted using our new system on various color images, arbitrarily selected from multiple databases, have yielded satisfactory and highly promising results.
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.
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.
Fuzzy integral tracking control of an activated sludge process Bekaik, Mounir; Bouras, Hichem; Hamana, Ahmed Sami
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.pp5083-5093

Abstract

This paper addresses the issue of tracking the output of an activated sludge process using fuzzy integral control. First, the dynamics of the nonlinear process are modeled with a dynamic state space fuzzy model integrating the effect of external disturbances, and then an additional integral state of the output tracking error is introduced to obtain an augmented Takagi-Sugeno (TS) fuzzy model. The TS fuzzy model is able to describe the dynamics of complex nonlinear systems with an excellent degree of accuracy. It is formulated by fuzzy if-then rules which can give local linear representation of the overall nonlinear system. Second, the design of the fuzzy integral control is performed, in which the state feedback gains are obtained by solving linear matrix inequalities (LMI). The objective is to ensure trajectory tracking of an activated sludge process (ASP) by controlling two key variables: the substrate concentration and the level of dissolved oxygen. To assess the performance of the proposed control strategy, a comparative analysis is carried out with a gain scheduling PI (GS-PI) controller. Simulation results are provided to illustrate the effectiveness of the proposed approach. Where, the fuzzy integral control reduces the high energy consumption in water treatment plants.
Fortifying network security: machine learning-powered intrusion detection systems and classifier performance analysis Tawil, Arar Al; Al-Shboul, Lara; Almazaydeh, Laiali; Alshinwan, Mohammad
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.pp5894-5905

Abstract

Intrusion detection systems (IDS) protect networks from threats; they actively monitor network activity to identify and prevent malicious actions. This study investigates the application of machine learning methods to strengthen IDS, explicitly emphasizing the comprehensive CICIDS 2017 dataset. The dataset was refined by implementing stringent preprocessing methods such as feature normalization, class imbalance management, feature reduction, and feature selection to ensure its quality and lay the foundation for developing robust models. The performance evaluation of three classifiers-support vector machine (SVM), extreme gradient boosting (XGBoost), and naive Bayes was highly impressive. Vital accuracy, precision, recall, and F1-score values of 0.984389, 0.984479, 0.984375, and 0.984304, respectively, were achieved by SVM. Notably, XGBoost demonstrated exceptional performance across all metrics, attaining flawless scores of 1.0. naive Bayes demonstrated noteworthy accuracy, precision, recall, and F1-score performance, which were recorded as 0.877392, 0.907171, 0.877007, and 0.876986, respectively. The results of this study emphasize the critical importance of preparation methods in improving the effectiveness of IDS via machine learning. This further demonstrates the potential of particular classifiers to detect and prevent network intrusions efficiently, thereby substantially contributing to cybersecurity measures.

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