Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

23,598 Article Results

Improving imbalanced class intrusion detection in IoT with ensemble learning and ADASYN-MLP approach

10.11591/ijeecs.v36.i2.pp1209-1217
Soni Soni , Muhammad Akmal Remli , Kauthar Mohd Daud , Januar Al Amien
The exponential growth of the internet of things (IoT) has revolutionized daily activities, but it also brings forth significant vulnerabilities. intrusion detection systems (IDS) are pivotal in efficiently detecting and identifying suspicious activities within IoT networks, safeguarding them from potential threats. It proposes a ensemble approach aimed at enhancing model performance in such scenarios. Recognizing the unique challenges posed by imbalanced class distribution, the research employs three sampling techniques LightGBM adaptive synthetic sampling (ADASYN) with multilayer perceptron (MLP), XGBoost ADASYN with MLP, and LightGBM ADASyn with XGBoost to address class imbalance effectively. Evaluation confusion matrix performance metrics underscores the efficacy of ensemble models, particularly LightGBM ADASYN with MLP, XGBoost ADASYN with MLP, and LightGBM ADASYN with XGBoost, in mitigating imbalanced class issues. The LightGBM ADASYN with MLP model stands out with 99.997% accuracy, showcasing exceptional precision and recall, demonstrating its proficiency in intrusion detection within minimal false positives negatives. Despite computational demands, integrating XGBoost within ensemble frameworks yields robust intrusion detection results, highlighting a balanced trade-off between accuracy, precision, and recall. This research offers valuable insights into the strengths with different ensemble models, significantly contributing to the advancement of accurate and reliable IDS in realm of IoT.
Volume: 36
Issue: 2
Page: 1209-1217
Publish at: 2024-11-01

An interactive visualization tool for the exploration and analysis of multivariate ocean data

10.11591/ijeecs.v36.i2.pp1329-1337
Preetha K. G. , Saritha S. , Jishnu Jeevan , Chinnu Sachidanandan , P. A. Maheswaran
Ocean data exhibits great heterogeneity from variances in measuring methods, formats, and quality, making it extremely complicated and diverse due to a variety of data kinds, sources, and study elements. A few examples of data sources are satellites, buoys, ships, self-driving cars, and distant systems. The processing of data is made more challenging by the significant regional and temporal variations in oceanic characteristics including temperature, salinity, and currents. This work presents an interactive tool for multivariate ocean parameter visualisation, specifically overlays, based on Python. In ocean data visualisation, overlays are extra visual layers or data points that are layered to improve comprehension over a basic map. Based on the available data and the visualisation goals, these overlays are chosen and blended. Users can customise overlays with this tool, which also supports formatting, 2D and 3D visualisation, and data preparation. In order to reduce artefacts, it uses kriging interpolation for 3D visualisation and a modified version of the ray casting algorithm for representing octree data. By integrating overlays like as bathymetry, currents, temperature, and marine life, users can produce visually appealing and comprehensive depictions of ocean data. This method provides a thorough grasp of intricate marine processes by making it easier to see patterns, trends, and abnormalities in the data.
Volume: 36
Issue: 2
Page: 1329-1337
Publish at: 2024-11-01

Characterization of UF-18 cacao pods using Arduino-based load compressor testing machine

10.11591/ijeecs.v36.i2.pp741-748
Maricel Gamolo Dayaday , Renel M. Alucilja , Ritchell Joy T. Cuarteros , Jeffrey A. Lavarias
Bean damage is one of the primary concerns in the pod-breaking process. Studies for pod-breaking machines are ongoing to ensure that the products made from these machines are of good quality. The objective of the study is to determine the physical and mechanical characteristics of the UF-18 pod. The Arduino-based load compressor testing machine was designed and developed to characterize the UF-18 pod. It was found that the average geometric mean diameter, surface area, and sphericity index of 115.37 mm, 41,899.48 mm², and 0.6372, respectively, and with a variation of ±27.17, ±14538133.04, and ±0.00038 respectively. Furthermore, the cacao pod samples had an average dimension of 181.29 mm, 94.26 mm, 90.01 mm, and 17.44 mm measured for the length, equatorial diameter, intermediate diameter and external thickness, respectively. Different pod sizes and thicknesses require various forces ranging from 36.94 to 92.42 kg (362.38 N to 906.64 N) and time ranging from 6-11 seconds to be able to break the pods. Determining the physical and mechanical properties of cacao pods enables fabricators to design efficient machines, which lessens the force to break and the damage to the beans, thus producing quality beans.
Volume: 36
Issue: 2
Page: 741-748
Publish at: 2024-11-01

Improved search method for classified reusable components on cloud computing

10.11591/ijeecs.v36.i2.pp1092-1104
Adnan Rawashdeh , Mouhammd Alkasassbeh , Radwan Dwairi , Hani Abu-Salem , Hashem Al-Mattarneh
Expanding development environments to accommodate huge amounts of reusable components along with associated maintenance and evolution responsibilities has become difficult and costly for software organizations to cope with, while benefits are limited to owner organizations. The challenge of organizing reusable assets so that finding the right component needed has always been a big challenge. The literature of software reuse lacks a comprehensive search method that is efficient and covers the entire system development lifecycle (SDLC). This research work attempts to make an efficient use of the cloud computing advantages and thus, encourages the migration of reusable components to the clouds. The maintenance, the search process and cost-related problems encountered with traditional in-house development environments can be resolved conclusively on the cloud. This research work proposes a multi-classification and clusters approach to migrate reusable components to the cloud. Accordingly, it applies indexing process to classified reusable components achieving efficient search. In addition, the proposed approach adopts a comprehensive SDLC-based classification to organize reusable components so that searching and finding an appropriate component becomes an easy task due to the fact it is bound to the particular undergoing phase. Cloud computing provides more storage and resources with low cost, compared to traditional in-house development environments.
Volume: 36
Issue: 2
Page: 1092-1104
Publish at: 2024-11-01

Experimental of information gain and AdaBoost feature for machine learning classifier in media social data

10.11591/ijeecs.v36.i2.pp1172-1181
Jasmir Jasmir , Dodo Zaenal Abidin , Fachruddin Fachruddin , Willy Riyadi
In this research, we use several machine learning methods and feature selection to process social media data, namely restaurant reviews. The selection feature used is a combination of information gain (IG) and adaptive boosting (AdaBoost) which is used to see its effect on the classification performance evaluation value of machine learning methods such as Naïve Bayes (NB), K-nearest neighbor (KNN), and random forest (RF) which is the aim of this research. NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computation, memory limitations, and ignoring irrelevant attributes. Then RF has weaknesses, including that the evaluation value can change significantly with only small data changes. In text classification, feature selection can improve the scalability, efficiency and accuracy of text classification. Based on tests that have been carried out on several machine learning methods and a combination of the two selection features, it was found that the best classifier is the RF algorithm. RF produces a significant increase in value after using the IG and AdaBoost features. Increased accuracy by 10%, precision by 12.43%, recall by 8.14% and F1-score by 10.37%. RF also produces even accuracy, precision, recall, and F1-score values after using IG and AdaBoost with an accuracy value of 84.5%; precision of 85.58%; recall was 86.36%; and F1-score was 85.97%.
Volume: 36
Issue: 2
Page: 1172-1181
Publish at: 2024-11-01

Forecasting research influence: a recurrent neural network approach to citation prediction

10.11591/ijeecs.v36.i2.pp1070-1082
Naser Jamal , Mohammad Alauthman , Muhannad Malhis , Abdelraouf M. Ishtaiwi
As the volume of scientific publications continues to proliferate, effective evaluation tools to determine the impact and quality of research articles are increasingly necessary. Citations serve as a widely utilized metric for gauging scientific impact. However, accurately prognosticating the long-term citation impact of nascent published research presents a formidable challenge due to the intricacy and unpredictability innate to the scientific ecosystem. Sophisticated machine learning methodologies, particularly recurrent neural networks (RNNs), have recently demonstrated promising potential in addressing this task. This research proposes an RNN architecture leveraging encoder-decoder sequence modeling capabilities to ingest historical chronicles and predict succeeding evolution via latent temporal dynamics learning. Comparative analysis between the RNN approach and baselines, including random forest, support vector regression, and multi-layer perceptron, demonstrate superior performance on unseen test data and rigorous k-fold cross-validation. On a corpus from Petra University, the RNN methodology attained the lowest errors (root mean squared error (RMSE) 1.84) and highest accuracy (0.91), area under the curve (AUC) (0.96), and F1-score (0.92). Statistical tests further verify significant improvements. The findings validate our deep learning solution's efficacy, robustness, and real-world viability for long-term scientific impact quantification to aid stakeholders in research evaluation. The findings intimate that RNN-based predictive modeling constitutes a potent technology for citation-driven scientific impact quantification.
Volume: 36
Issue: 2
Page: 1070-1082
Publish at: 2024-11-01

Optimizing network lifetime in wireless sensor networks: a hierarchical fuzzy logic approach with LEACH integration

10.11591/ijeecs.v36.i2.pp1140-1148
Chandrika Dadhirao , Ram Prasad Reddy Sadi , B V A N S S Prabhakar Rao , Panduranga Vital Terlapu
Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols.
Volume: 36
Issue: 2
Page: 1140-1148
Publish at: 2024-11-01

Comparing feature usage in IMU-based gesture control for omnidirectional robot via wearable glove

10.11591/ijres.v13.i3.pp542-551
Dahnial Syauqy , Eko Setiawan , Edita Rosana Widasari
To improve the intuitiveness of maneuver control on omniwheeled mobile robot, many hand gesture-based robot controls have been developed. The focus of this research is to develop a wearable system for data acquisition from inertial measurement unit (IMU) sensors and compare its features to be used as gesture recognition using the random forest algorithm. With the need of resource constrained device for wearable system based on microcontrollers, we compared the use of Euler and quaternion-based orientation data as input features. As additional comparison, dimension reduction was also carried out using the principal component analysis (PCA) method. Hand gestures are recognized using data obtained by the IMU sensor embedded in the wearable glove. This study compared the accuracy and size of library files embedded in microcontrollers in several feature usage scenarios. The test evaluation results of all scenarios show that the use of all features provides a balance between high accuracy but small file sizes, respectively 99% and 9.2 KB. However, the use of other fewer features, such as by only using 3 Euler data, 4 quaternion data, or by using PCA algorithm (PC=3) can also be used since the accuracy is still above 90%, with a relatively larger file size.
Volume: 13
Issue: 3
Page: 542-551
Publish at: 2024-11-01

Sliding mode control for the speed loop combined with adaptive coefficients for urban trains’ load variations of Nhon – Hanoi Station Metro line

10.11591/ijece.v14i5.pp5030-5037
An Thi Hoai Thu Anh , Tran Hung Cuong , Ha Van Dinh
Electric trains are becoming increasingly popular due to their environmental protection and ability to transport a large number of passengers. Alongside this trend, traction motors for electric trains have become diverse thanks to the rapid development of power electronics. Among them, the permanent magnet synchronous motor (PMSM) stands out with advantages such as high efficiency, high torque-to-current ratio, and compactness compared to other motors of the same power, making it the top choice. However, PMSM motors are nonlinear objects, so the nonlinear control technique of sliding mode control has been applied to the speed loop in this paper. Additionally, electric trains' inertial torque and load torque vary due to changes in the number of passengers during peak and off-peak hours and weather conditions. Therefore, this paper introduces two adaptive coefficients to account for these variations. Simulation results show that the sliding mode control technique for the speed loop circuit provides a faster and more accurate speed response. Meanwhile, the two parameters also adapt to the inertial and load torque variations. This ensures the safety and efficiency of the electric train system, contributing to the advantages of this mode of transportation.
Volume: 14
Issue: 5
Page: 5030-5037
Publish at: 2024-10-01

The comparison of several cryptosystems using the elliptic curve: a report

10.11591/ijece.v14i5.pp5319-5329
Mai Manh Trung , Le Phe Do , Do Trung Tuan , Thu Thuy Trieu , Nguyen Van Tanh , Ngo Quang Tri , Bui Van Cong
The elliptic curve cryptosystem (ECC) has several applications in Information Security, especially in cryptography with two main activities including encrypting and decrypting. There were several solutions of different research teams which propose various forms of the elliptic curve cryptosystem on cryptographic sector. In the paper, we proposed a solution for applying the elliptic curve on cryptography which is based on these proposals as well as basic idea about the elliptic curve cryptosystem. We also make comparison between our proposal and other listed solution in the same application of the elliptic curve for designing encryption and decryption algorithms. The comparison results are based on parameters such as time consumption (t), RAM consumption (MB), source code size (Bytes), and computational complexity.
Volume: 14
Issue: 5
Page: 5319-5329
Publish at: 2024-10-01

Alzheimer’s prediction via CNN-SVM on chatbot platform with MRI

10.11591/ijeecs.v36.i1.pp64-73
Muhammad Syaekar Kadafi , Ahmad Khalil Yaqubi , Purbandini Purbandini , Suryani Dyah Astuti
Artificial intelligence (AI), consisting of models and algorithms capable of concluding data to produce future predictions, has revolutionary potential in various aspects of human life. One application is an Alzheimer’s disease (AD) prediction chat robot (chatbot). Only now has a method provided very accurate findings and recommendations regarding the early detection of AD using magnetic resonance imaging (MRI). Therefore, this research aims to measure AD prediction performance in four stage classes, namely very mild demented, mild demented, moderate demented, and non-demented, using brain MRI images trained in the convolutional neural network (CNN)- support vector machine (SVM) model. The research involved nine combination schemes of dataset proportions and preprocessing in the CNNSVM model. Evaluation shows that scheme 1 produces the highest accuracy, precision, recall, and F1-score, namely 98%, 99%, 98%, and 98%. The chatbot, trained using CNN, achieved 99.34% accuracy in question responses, and was then combined with AD prediction models for improved accuracy. The test results show that the chatbot functionality runs well for each transition, with a functionality score reaching 99.64 points out of 100.00. This success shows excellent potential for early detection of AD. This research brings new hope in preventing AD through AI, with potential positive impacts on human health and quality of life.
Volume: 36
Issue: 1
Page: 64-73
Publish at: 2024-10-01

Machine learning-driven stock price prediction for enhanced investment strategy

10.11591/ijece.v14i5.pp5884-5893
Omaima Guennioui , Dalila Chiadmi , Mustapha Amghar
Forecasting stock prices, a task complicated by the inherent volatility of the stock market, poses a significant challenge. The ability to accurately forecast stock prices is crucial, as it provides investors with crucial insights, enabling them to make informed strategic decisions. In this paper, we propose a novel investment strategy that relies on predicting stock prices. Our approach utilizes a hybrid predictive model that combines light gradient-boosting machine (LightGBM) and extreme gradient boosting (XGBoost). This model is designed to generate short to medium-term forecasts for a wide range of stocks. The strategy has shown promising results, surpassing the local market indices used as benchmarks in terms of both risk and return. Our findings demonstrate the strategy's effectiveness in both upward and downward market trends, underscoring its potential as a robust tool for portfolio management in diverse market conditions.
Volume: 14
Issue: 5
Page: 5884-5893
Publish at: 2024-10-01

Single phase robustness variable structure load frequency controller for multi-region interconnected power systems with communication delays

10.11591/ijece.v14i5.pp5064-5071
Phan-Thanh Nguyen , Cong-Trang Nguyen
This paper proposes an estimator-based single phase robustness variable structure load frequency controller (SPRVSLFC) for the multi-region interconnected power systems (MRIPS) with communication delays. The key attainments of this research consist of two missions: i) a global stability of the power systems is guaranteed by removing the reaching phase in traditional variable structure control (TVSC) technique; and ii) a novel output feedback load frequency controller is established based on the estimator tool and output information only. Initially, a single-phase switching function is constructed to disregard the reaching phase in TVSC. Then, an unmeasurable state variable of the MRIPS is estimated by using the proposed estimator tool. Next, a new SPRVSLFC for the MRIPS is suggested based on the support of the estimator tool and output data only. Furthermore, a sufficient constraint is constructed by retaining the linear matrix inequality (LMI) procedure for ensuring the robust stability of motion dynamics in sliding mode. Finally, the performance of interconnected power plant under changed multi-constraints is imitated with the novel control technique to validate the practicability of the plant.
Volume: 14
Issue: 5
Page: 5064-5071
Publish at: 2024-10-01

Supply and demand of ecosystem service provision in relation to dynamics land-cover changes: a remote sensing and geospatial analysis in Sukabumi Regency

10.11591/ijece.v14i5.pp5728-5737
Ananda Fitriani , Muhammad Dimyati , Faris Zulkarnain
The rate of population growth in Sukabumi Regency continues to grow, along with the increasing need for food. This population growth, combined with the constant changes in land cover can reduce the productivity of environment in providing natural capital for food availability. Therefore, this study aimed to examine the condition of ecosystem service provision for a decade in Sukabumi Regency due to changes in land cover. In general, the efficient use of remote sensing method and geographic information systems to monitor ecosystem services had received widespread recognition. Following this scenario, the current study used geospatial analysis with dasymetric method which was integrated with supply and demand formulas for ecosystem services provision, food availability, and remote sensing. Geographic information system was also used for land cover interpretation data. The results showed that three districts in Sukabumi Regency, namely Cicurug, Cibadak, and Cicantayan, had “exceeded” condition when the environmental condition already passed the threshold or were unable to support population's needs. Meanwhile, the other districts have “not exceeded” condition, when the environmental conditions were still able to fulfill the needs of population. Finally, the changes in agricultural land cover had a significant influence on the condition of ecosystem services.
Volume: 14
Issue: 5
Page: 5728-5737
Publish at: 2024-10-01

The research on the signal source number estimation algorithm

10.11591/ijeecs.v36.i1.pp188-196
Wang Peizhi , Raihani Mohamed , Norwati Mustapha , Noridayu Manshor
In array signal processing, Estimating the quantity of signal sources represents a crucial area of investigation. In this paper, a comprehensive introduction and analysis of the estimation methods for determining the number of signal sources are presented, including the background and significance, and the significance of precise estimation of the quantity of signal sources. The influence of factors such as signal-to-noise ratio (SNR), noise background, and number of snapshots on the estimation algorithm is discussed in detail. At the same time, common array models are introduced. Then, different signal source number estimation algorithms are analyzed in detail, and their respective advantages and applicable conditions are pointed out. Finally, the performance of each algorithm in different situations is evaluated by comparing the performance of the algorithms under different SNRs, snapshot numbers, and array elements. The experimental results show that with the increase of the SNR and the number of array elements, the correct estimation probability of the algorithm also increases correspondingly, which provides a reliable experimental basis and performance evaluation for the estimation.
Volume: 36
Issue: 1
Page: 188-196
Publish at: 2024-10-01
1 2 3
...
1573 1574
Show 11 of 1574

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration